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Sakas, D.P.; Giannakopoulos, N.T.; Terzi, M.C.; Kanellos, N. Supply Chain Transportation Indexes through Big Data. Encyclopedia. Available online: https://encyclopedia.pub/entry/49234 (accessed on 16 November 2024).
Sakas DP, Giannakopoulos NT, Terzi MC, Kanellos N. Supply Chain Transportation Indexes through Big Data. Encyclopedia. Available at: https://encyclopedia.pub/entry/49234. Accessed November 16, 2024.
Sakas, Damianos P., Nikolaos T. Giannakopoulos, Marina C. Terzi, Nikos Kanellos. "Supply Chain Transportation Indexes through Big Data" Encyclopedia, https://encyclopedia.pub/entry/49234 (accessed November 16, 2024).
Sakas, D.P., Giannakopoulos, N.T., Terzi, M.C., & Kanellos, N. (2023, September 15). Supply Chain Transportation Indexes through Big Data. In Encyclopedia. https://encyclopedia.pub/entry/49234
Sakas, Damianos P., et al. "Supply Chain Transportation Indexes through Big Data." Encyclopedia. Web. 15 September, 2023.
Supply Chain Transportation Indexes through Big Data
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This entry is adapted from the peer-reviewed paper 10.3390/app13179983

Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization.

supply chain modeling supply chain analytics operation optimization deep learning feedforward neural network (FNN) big data

1. Supply Chain Transportation Indexes Importance

Supply chain transportation is an important indicator of economic development from both a macroeconomic and microeconomic perspective, as well as one of the backbones of international trade, able to predict future trends and identify new opportunities for production, distribution, and consumption [1]. According to Statista [2], the market size of the logistics sector is worth ten trillion U.S. dollars globally and is projected to exceed 14.08 trillion U.S. dollars by 2028. These thrilling statistics, coupled with the evolution of smart technologies, come with both opportunities and threats. On the one hand, the explosion of Industry 4.0, which is associated with various innovations, has expanded businesses’ operations by opening up new markets and increasing the customer base [3]. On the other hand, the occurrence of environmental factors, such as natural disasters and the COVID-19 pandemic, has affected the smooth operations of the industry, resulting in disruptions and increased vulnerabilities [4].
Until recently, the focal point of supply chain management was mainly cost-centered [5]. However, improving the performance of the supply chain requires more than just cutting costs. The elevation of the supply chain to become a source of competitive advantage has forced supply chain firms to adopt an integrated profile in which customer satisfaction is equally important [6]. This holistic approach has forced supply chain transportation firms to establish an environment of consistent supply chain visibility to avoid potential supply chain breakdowns while simultaneously meeting or exceeding customers’ expectations by offering exceptional services.
The turn of the decade with the onset of the pandemic has highlighted the role of reliability and resilience as key quality factors for the optimization of supply chain transportation [7]. Such optimization is based on specific transportation indexes that assess the health of the supply chain. According to the Bureau of Transportation Statistics, “The Bureau of Transportation Statistics (BTS) Transportation Services Index (TSI), the Dow Transportation Index, and the Cass Freight Index represent transportation economic indicators that reflect the responses of transportation providers to the economy’s demands for moving freight and/or passengers” [8]. There are several transportation indexes, such as the Freight Transportation Service Index (TSI), the Baltic Dry Index (BDI), the China Containerized Freight Index (CCFI), and the Air Cargo Index (ACI), among others, that serve as important tools to analyze transportation trends and assess the overall supply chain’s transportation efficiency.
Lu et al. [9], acknowledge the National Logistics Performance Index (LPI) as “an interactive benchmarking tool to identify possible challenges and opportunities about the performance of trade logistics”. Kumar & Anbanandam [10], have developed a social sustainability index for freight transportation systems. Azevedo et al. [11], propose the LARG index as a risk management tool to respond rapidly and cost-effectively to unpredictable changes and unexpected disturbances within the automotive supply chain. To this end, the performance of the supply chain is affected by specific transportation indexes.
To sum up, supply chain transportation indexes are essential tools for performance measurement, economic indicators, industry benchmarking, and risk management that enable businesses to optimize their logistics operations, adapt to changing market conditions, and maintain a competitive advantage. Although several researchers have investigated the sustainability performance of the supply chain through key transportation performance indexes [12], there are still some burning questions in mind. Have global supply chains adjusted to the new digital normal? What is the impact on supply chain transportation indexes due to the advancement of cutting-edge technologies, such as big data? Are supply chain transportation firms equipped to optimize their operations by unlocking the dynamics of behavioral analytics? Despite the vast literature on supply chain transportation operations [13], descriptive, predictive, and prescriptive analytics considering the optimization of key transportation indexes through big data analytics and deep learning techniques such as SVM, KNN, CNN, LSTM models, etc., are still lacking.
Existing research regarding supply chain transportation issues only concerns how to maximize profits and improve performance [14][15], providing less information on customers’ digital behavior. Since companies are now moving to customer-centricity approaches to remain competent and improve performance outcomes [16], adopting behavioral analytics metrics becomes of the essence. The accuracy of supply chain customers’ demands through deep and machine learning contexts, such as SVM, naïve Bayers, KNN, CNN, and LSTM models, could enhance the efficiency and performance of supply chain firms [17]. Moreover, the reduction in inventory storage costs and the increase in supply chain firms’ profits are among the other benefits of deep learning procedures’ capitalization (such as LSTM and LGBM models) combined with big data analytics [18]. Lastly, deep learning methodologies, such as CNN and SBO models, in the supply chain context tend to remove uncertainties that are based on firms’ expenses, competitiveness, operational efficiency, etc. [19].

2. Website Big Data Analytics and Deep Learning

The popularity of social media generates a tsunami of digital data, which is perceived as a challenging process to analyze and manage [20]. Even though big data offers an ocean of opportunities due to its transformational impact on various sectors, it further brings challenges on how to harness the available data, especially regarding data mining and information processing [21]. To mitigate the uncertainties generated by big data analytics, artificial intelligence techniques, such as evolutionary algorithms (EAs) and artificial neural networks (ANNs), have emerged in an attempt to provide accurate, factual, and scalable results [22].
Ning & Yu [23], acknowledge the potential of leveraging deep learning techniques, such as variational autoencoders (VAEs), for hedging against uncertainty in data-driven optimization. Deep learning plays a key role in providing big data predictive analytics solutions, as shown with Restricted Boltzmann Machines (RBMs) exploitation [24]. Deep learning is used to analyze both structured and unstructured data by identifying new patterns and developing knowledge [25]. Especially for the supply chain industry, as data continues to grow exponentially, it will continue to be exploited to unearth new patterns, understand potential causes, and interpret the available data.
Organizations within the supply chain industry have to invest in innovative approaches that will prevent operational inefficiencies, poor customer satisfaction, and revenue loss. Technology is one of the key trends that impacts the sustainability of the supply chain. As such, artificial intelligence and big data have the potential to become a powerful analytics pairing [26], a tool for companies that aspire to obtain the most out of their data analytics.
In summary, big data analytics provide the necessary infrastructure and scalability, while deep learning leverages the power of big data to train models and perform advanced analytics tasks. Therefore, overcoming supply chain disruptions and ensuring the sustainability of the sector lie in the proper management of data growth into organized intelligence [27][28]. Until now, even if big data offers great potential for revolutionizing all aspects of supply chain firms and immense value to different industries, there is still uncertainty over its use [29]. To maximize resilience, secure agility, and achieve the optimization of the supply chain transportation industry, companies need to invest in emerging technologies to develop innovative, data-driven applications based on both big data and deep learning techniques.

3. Supply Chain Operations’ Engineering through Deep Learning

Supply chain operations engineering involves the design, analysis, and optimization of the supply chain elements and processes to ensure the efficient flow of goods and services from suppliers to end customers [30]. With the revolution of smart technologies, supply chain engineering should be seen as an innovation able to achieve radical business transformation [31]. In other words, this is a common ground for applied sciences, information technology, and supply chain management to increase the effectiveness of supply chain demand while simultaneously adding flexibility and decreasing costs [32].
During the past years, numerous companies have been rocked by unforeseen supply-chain vulnerabilities and disruptions, leading to revenue loss and poor customer satisfaction [33]. In an attempt to change the current landscape and balance competing goals such as risk, vulnerability, and financial risk against reliability and operational efficiency [34], deep learning methods such as variational autoencoders (VAEs) can be utilized. By using applied sciences, firms could leverage the dynamics of immersive technology to analyze and optimize elements and processes of the supply chain to meet specific business requirements. When applied to the supply chain, deep learning algorithms, such as deep learning-augmented decision-making (DLADM), enhance the efficiency of companies and improve the decision-making process [35].
Recently, the deep learning algorithm of the multilayer feedforward artificial neural network (MLFANN) has been used in a study to estimate customers’ demand [5]. The authors highlight the importance of using deep learning in developing an improved demand forecasting model based on customers’ behavior through historical sales data. Identifying when customers obtain it is the key factor for business performance in the digital age [36]. For example, businesses can use algorithms powered by deep learning to examine past sales data, purchasing behaviors, and other key information to forecast supply chain demand. This data can be utilized to discover new behavioral patterns, ensuring the overall advancement and sustainability of the supply.
Despite their great importance, deep learning methods, such as deep neural networks (DNNs), are challenged by the need to use large quantities of data to validate the outcome [37]. Tolk [38] has introduced the concept of combining big data, deep learning methods (SVM, KNN, CNN, and LSTM models), and simulation as a holistic approach to supporting observation, analysis, and application for unbiased data evaluation by repeatable mechanisms. To this end, the current paper has developed an agent-based model based on an ever-increasing number of agents to validate the outcomes, reduce potential errors, and optimize the simulation results. The agents represent the visitors of a supply chain firm’s website, and the hybrid simulation model applies FNN deep learning processes by adjusting the number of website visits and the number of website visitors to optimize the paper’s transportation indexes.

References

  1. Bekrar, A.; Ait El Cadi, A.; Todosijevic, R.; Sarkis, J. Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective. Sustainability 2021, 13, 2895.
  2. Statista. Logistics Industry Worldwide—Statistics & Facts. 2023. Available online: https://www.statista.com/topics/5691/logistics-industry-worldwide/#editorsPicks (accessed on 20 May 2023).
  3. Fatorachian, H.; Kazemi, H. Impact of Industry 4.0 on supply chain performance. Prod. Plan. Control 2020, 32, 63–81.
  4. Ali, I.; Arslan, A.; Khan, Z.; Tarba, S.Y. The Role of Industry 4.0 Technologies in Mitigating Supply Chain Disruption: Empirical Evidence From the Australian Food Processing Industry. IEEE Trans. Eng. Manag. 2021, 1–11.
  5. Kilimci, Z.H.; Akyuz, A.O.; Uysal, M.; Akyokus, S.; Uysal, M.O.; Bulbul, B.A.; Ekmis, M.A. An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain. Complexity 2019, 2019, 9067367.
  6. Uvet, H. Importance of Logistics Service Quality in Customer Satisfaction: An Empirical Study. OSCM Publ. 2020, 13, 1–10.
  7. Korepin, V.; Dzenzeliuk, N.; Seryshev, R.; Rogulin, R. Improving supply chain reliability with blockchain technology. Marit. Econ. Logist. 2021, 1–14.
  8. Bureau of Transportation Statistics. What the Transportation Services Index, Dow Transportation Index, and Cass Freight Index Tell Us. 2019. Available online: https://www.bts.gov/topics/transportation-and-economy/what-transportation-services-index-dow-transportation-index-and (accessed on 20 May 2023).
  9. Lu, M.; Xie, R.; Chen, P.; Zou, Y.; Tang, J. Green Transportation and Logistics Performance: An Improved Composite Index. Sustainability 2019, 11, 2976.
  10. Kumar, A.; Anbanandam, R. Development of social sustainability index for freight transportation system. J. Clean. Prod. 2019, 210, 77–92.
  11. Azevedo, S.G.; Carvalho, H.; Cruz-Machado, V. LARG index: A benchmarking tool for improving the leanness, agility, resilience and greenness of the automotive supply chain. Benchmarking Int. J. 2016, 23, 1472–1499.
  12. Singh, C.S.; Soni, G.; Badhotiya, G.K. Performance indicators for supply chain resilience: Review and conceptual framework. J. Ind. Eng. Int. 2019, 15, 105–117.
  13. Dutta, P.; Choi, T.-M.; Somani, S.; Butala, R. Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transp. Res. Part E Logist. Transp. Rev. 2020, 142, 102067.
  14. Trivellas, P.; Malindretos, G.; Reklitis, P. Implications of Green Logistics Management on Sustainable Business and Supply Chain Performance: Evidence from a Survey in the Greek Agri-Food Sector. Sustainability 2020, 12, 10515.
  15. Lee, K.L.; Romzi, N.; Hanayasha, J.R.; Alzoubi, H.M. Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia. Uncertain Supply Chain Manag. 2022, 10, 537–550.
  16. Guptra, S.; Ramachandran, D. Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach. J. Retail. 2021, 97, 597–620.
  17. Emmanuel, G.; Ramadhan, A.; Zarlis, M.; Abdurachman, E.; Trisetyarso, A. Machine learning in drug supply chain management during disease outbreaks: A systematic review. Int. J. Electr. Comput. Eng. 2023, 13, 5517–5533.
  18. Saha, P.; Gudheniya, N.; Mitra, R.; Das, D.; Narayana, S.; Tiwari, M.K. Demand Forecasting of a Multinational Retail Company using Deep Learning Frameworks. IFAC Pap. 2022, 55, 395–399.
  19. Pereira, M.M.; Frazzon, E.M. A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. Int. J. Inf. Manag. 2021, 57, 102165.
  20. Jan, B.; Farman, H.; Khan, M.; Imran, M.; Islam, I.U.; Ahmad, A.; Ali, S.; Jeon, G. Deep learning in big data Analytics: A comparative study. Comput. Electr. Eng. 2019, 75, 275–287.
  21. Zhang, Q.; Yang, L.T.; Chen, Z.; Li, P. A survey on deep learning for big data. Inf. Fusion 2018, 42, 146–157.
  22. Hariri, R.H.; Fredericks, E.M.; Bowers, K.M. Uncertainty in big data analytics: Survey, opportunities, and challenges. J. Big Data 2019, 6, 44.
  23. Ning, C.; You, F. Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming. Comput. Chem. Eng. 2019, 125, 434–448.
  24. Chen, X.-W.; Lin, X. Big Data Deep Learning: Challenges and Perspectives. IEEE Access 2014, 2, 514–525.
  25. Olson, D.L. A review of supply chain data mining publications. JSCMS 2020, 1, 15–26.
  26. Sanders, N.R.; Boone, T.; Ganeshan, R.; Wood, J.D. Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities. J. Bus. Logist. 2019, 40, 229–240.
  27. Al-Shbail, T.; Maghayreh, A.; Awad, M. Big Data Analytics for Supply Chain Sustainability: Amid the Outbreak of the COVID-19 Pandemic. World Cust. J. 2021, 15, 61–72. Available online: https://worldcustomsjournal.org/Archives/Volume%2015%2C%20Number%202%20(Sep%202021)/1922%2001%20WCJ%20v15n2%20Shbail%20et%20al.pdf (accessed on 10 June 2023).
  28. Mageto, J. Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains. Sustainability 2021, 13, 7101.
  29. Tseng, M.-L.; Wu, K.-J.; Lim, M.K.; Wong, W.P. Data-driven sustainable supply chain management performance: A hierarchical structure assessment under uncertainties. J. Clean. Prod. 2019, 227, 760–771.
  30. Chang, S.E.; Chen, Y.-C.; Lu, M.-F. Supply chain re-engineering using blockchain technology: A case of smart contract based tracking process. Technol. Forecast. Soc. Chang. 2019, 144, 1–11.
  31. Hrytsenko, L.L.; Boronos, V.M.; Melnyk, M.; Ignatchenko, V.I.; Kovalev, Y.S. Organizational and Economic Capabilities of Business Processes Re-engineering in the Marketing Activities of Industrial Enterprises under the Conditions of Economy Financialization. Mech. Econ. Regul. 2019, 2, 30–41.
  32. Dolgui, A.; Proth, J.M. Supply Chain Engineering: Useful Methods and Techniques; Springer: London, UK, 2010.
  33. Shishodia, A.; Sharma, R.; Rajesh, R.; Munim, Z.H. Supply chain resilience: A review, conceptual framework and future research. Int. J. Logist. Manag. 2023, 34, 879–908.
  34. Ali, N.; Ghazal, T.M.; Ahmed, A.; Abbas, S.; Khan, M.A.; Alzoubi, H.M.; Farooq, U.; Ahmad, M.; Khan, M.A. Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques. Intell. Autom. Soft Comput. 2022, 31, 1671–1687.
  35. Shrestha, Y.R.; Krishna, V.; von Krogh, G. Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. J. Bus. Res. 2021, 123, 588–603.
  36. Woo, H.; Kim, S.J.; Wang, H. Understanding the role of service innovation behavior on business customer performance and loyalty. Ind. Mark. Manag. 2021, 93, 41–51.
  37. de Melo, C.M.; Torralba, A.; Guibas, L.; DiCarlo, J.; Chellappa, R.; Hodgins, J. Next-generation deep learning based on simulators and synthetic data. Trends Cogn. Sci. 2022, 26, 174–187.
  38. Tolk, A. The next generation of modeling & simulation: Integrating big data and deep learning. In Proceedings of the SummerSim ‘15: Proceedings of the Conference on Summer Computer Simulation, Chicago, IL, USA, 1–8 July 2015.
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Subjects: Operations Research & Management Science
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Damianos P. Sakas
,
Nikolaos T. Giannakopoulos
,
Marina C. Terzi
,
Nikos Kanellos
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Revision: 1 time (View History)
Update Date: 15 Sep 2023
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