Big Data in Logistics

Introduction

With the trend of the amount of data constantly increasing in a geometric progression, modern businesses have the chance to make the best use of Big Data initiatives and research Big Data as one of the most important fields. The paradigm of Big Data is based on the idea that specific types of knowledge can be extracted from information collected from various sources (Wang et al., 2016). Later, that information can be introduced into decision-making systems to support the value of intelligent services and encompass additional research topics. For instance, many businesses are currently utilizing such instruments as data mining, data analysis, machine learning, and many other tools that are based on Big Data.

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On a larger scale, the value of Big Data technologies for the field of logistics can be explained by the growing amounts of data that logisticians generate daily. The process of optimizing this information is also important as it can make information more accessible than before and give an upper hand in terms of dealing with supply chain risks and processing network data efficiently. The comprehensive knowledge that logistics companies obtain from using Big Data is perfectly balanced by several threats associated with the implementation of this technology (Witkowski, 2017). The concept of Big Data, therefore, is rather controversial and requires multilateral analytics, as additional insights could be the key to improving customer experience, delivery time, and product quality.

Opportunities Linked to Big Data in Line Haul and Logistics

The advent of Crowd-Based Solutions

One of the opportunities that cannot be ignored when assessing the value of Big Data for logistics is the aptitude of a crowd to back up organizational initiatives and become a sort of a control device for the logistics company at hand. By using Big Data, logistics companies are attracting additional workforce to their operations and develop a business environment where startups have more chances to receive funding (Shang, Dunson, and Song, 2017). Another benefit of Big Data within the framework of crowdfunded logistics is the ability to receive essential resources from the crowd through intelligent networked research. When talking about a distribution network, for example, crowdsourced logistics solutions could bring a substantial difference through the potential enhancements of operational efficiency (e.g., delivery time and geolocation assessment).

The idea of crowd-based solutions that utilize Big Data in logistics is rather simple. Practically anyone can interfere with the delivery process and ensure effective last-mile delivery in the case where they are going in the same direction as the original package. The ability to scale the number of affiliates via promotion and occasional benefits can be seen as one of the smartest Big Data usage options of the past decade, as this approach to logistics significantly reduces the burden that is usually placed on logistics managers responsible for ensuring the most operational delivery routes (Suma et al., 2017).

Even though the number of existing incentives is low, the presence of crowd-based solutions showcases the potential of Big Data in terms of it being able to reduce delivery costs and improve the penetration capability of logistics companies even in rural areas.

Premeditated Network Scheduling

When considering the value of a business strategy, any logistics company should look at its distribution network as one of the key instruments allowing for demand forecasts. This happens because long amortization cycles are often attracting additional investments (Tiwari, Wee, and Daryanto, 2018). Accordingly, premeditated network scheduling can be achieved with the help of Big Data techniques that allow logisticians to assess the risk of investing and also analyze transportation routes and historical capacity. With the team having to consider numerous seasonal factors and learn new algorithms of freight flow, logistics companies utilize external economic information to improve the existing state of affairs through Big Data usage. For instance, this could relate to regional growth statistics or any other industry-specific information.

Looking at the capacity demand in line haul and logistics is critical. Therefore, Big Data allows for an improved capacity prediction pattern that allows the company to manage its transportation while also paying attention to the predictive value of all information obtained by the organization (Speranza, 2018). In a sense, Big Data can be exploited to develop scenario-modeling opportunities that can be used to turn data analysis into an organizational asset.

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Premeditated network scheduling, in turn, will reduce the risks associated with investments and organizational capacity, leaving more room for long-term decisions. Overall, Big Data is crucial for line haul and logistics because it offers automated feedback and brings dynamics to the industry, protecting the organization from unpredictable investments.

Risk Assessment and Resilience Planning

Another area where Big Data significantly contributes to the value of line haul and logistics activities is risk assessment. The rationale behind this statement is that the company should have a perfect understanding of its customers’ supply chains to predict potential challenges and opportunities (Pan et al., 2016). Big Data, therefore, serves as an instrument expected to improve the resilience of all logistics systems that collect and communicate any kind of information, contributing to the development of an intelligent risk assessment model. The latter, in turn, is going to affect the level of performance of any given supply chain owing to the exclusive insights from every other point in the system where data can be generated.

For line-haul and logistics in general, this becomes crucial when the team finishes aggregating and analyzing a superfluous number of sources that contain both local and dissociated data sets that could be of high importance to the organization (Yan et al., 2019). The lack of structure and updates for such information makes Big Data analytics one of the biggest players in logistics due to the ability to detect certain scenarios before they even break out. Therefore, different interrelated bits of information can be transformed into a detailed trend analysis where innovative processing techniques and semantic analytics for logistics events would be used.

Challenges Linked to Big Data in Line Haul and Logistics

Lack of Data Curation and Optimisation

Since Big Data seriously affected the processes of storing and capturing information, it is evident that data access mechanisms and storage architecture became inferior to the system’s capability to take on larger arrays of data (Wamba et al., 2018). The process of discovering new knowledge turned out to be beneficial to logistics and line haul operators, but the problem was that Big Data created bulky portions of information without giving the organizations an option of processing all that data efficiently.

In a sense, before gaining any valuable insights, Big Data users in line haul and logistics have to face off against the limitations of digital architectures that support the technology of Big Data. The latter is rather hard to optimize as the computational power of modern systems often does not meet the Big Data requirements and reduces organizational performance instead of boosting it (Barbosa et al., 2018). Therefore, the operations of data migration, replication, and distribution become unmanageable due to the lack of bandwidth capacity.

Increased Risk of Cybersecurity Breaches

The increasing amount of data has also led to the issue of cybersecurity breaches that can serve as the basis for confidential data loss and other problems related to Big Data (Kayikci, 2018). Almost any knowledgeable cybercriminal can gain access to vital personal information nowadays, leaving line haul and logistics operators vulnerable to losing data that is ultimately used to create value for the organization. Even though data relevance is a partially biased concept, the loss of any kind of private information related to customers, finance, manufacturing, or logistics could land a devastating blow on the management (Kayikci, 2018). All the correlations that can be found among Big Data recordsets negatively affect the level of accessibility, as in the case of a breach, unwanted information could be discovered in no time.

The Need for Real-Time Analysis and Decision Making in Line Haul and Logistics

The first reason why logistics and line haul could benefit from data-driven real-time analysis and decision-making is the fact that supply chains have to be optimized to the core to allow for the highest level of performance. This directly relates to the process of optimizing resource utilization and geographical coverage, while also making sure that delivery time is adequate and does not interfere with customers’ and stakeholders’ needs (Strandhagen et al., 2017).

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On the other hand, Big Data creates opportunities for improved data runs, meaning that new technologies increase the availability of certain insights and create possibilities to optimize the results of data analysis without affecting the time required to analyze the data arrays acquired. As for the real-time element of the logistics equation, Big Data is going to improve the predictive capabilities of organizations. The logistics industry is going to benefit from value-added resource management and capacity projection.

Another reason why line haul and logistics require the implementation of Big Data technologies is the ability to create a tangible environment where both customers and goods would be perceptible as well (Tao et al., 2018). The reason to include this point relates to the fact that physical delivery requires a tangible customer-agent interaction to ensure successful delivery and pickup operations.

The amount of data that is generated from interaction with customers grows daily while market intelligence gets improved as well. For line-haul and logistics, tangible Big Data means that the companies are going to generate valuable insights that can be used to increase product quality and impact consumer sentiment (Tao et al., 2018). With information on demographics and detailed product feedback, the value of Big Data cannot be underestimated.

The last reason why real-time decision-making and data analysis could be powered by Big Data is that this technology creates room for the development of a detailed delivery network (Papadopoulos et al., 2017).

The global flow of goods will be carefully monitored by line haul and logistics organizations that expect to optimize the network while also focusing on the micro-economic view of logistics. Overall, Big Data can be seen decentralizing logistics operations all over the world, increasing the industry’s coverage in a meaningful way while also helping logisticians collect and process large amounts of data (Papadopoulos et al., 2017). The different types of statistics that logistics companies get from using Big Data (demographic, traffic, or even environmental) can be later used to process customer requests quicker and improve organizational operations based on real-life data and not exclusively simulated prognostications.

References

Barbosa, M. W. et al. (2018) ‘Managing supply chain resources with Big Data Analytics: a systematic review’, International Journal of Logistics Research and Applications, 21(3), pp. 177-200.

Kayikci, Y. (2018) ‘Sustainability impact of digitization in logistics’, Procedia Manufacturing, 21, pp. 782-789.

Pan, Y. et al. (2016) ‘Urban big data and the development of city intelligence’, Engineering, 2(2), pp. 171-178.

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Papadopoulos, T. et al. (2017) ‘Big data and analytics in operations and supply chain management: managerial aspects and practical challenges’, Production Planning & Control, 28(11-12), pp. 873-876.

Shang, Y., Dunson, D. and Song, J. S. (2017) ‘Exploiting big data in logistics risk assessment via bayesian nonparametrics’, Operations Research, 65(6), pp. 1574-1588.

Speranza, M. G. (2018) ‘Trends in transportation and logistics’, European Journal of Operational Research, 264(3), pp. 830-836.

Strandhagen, J. O. et al. (2017) ‘Logistics 4.0 and emerging sustainable business models’, Advances in Manufacturing, 5(4), pp. 359-369.

Suma, S. et al. (2017) ‘Enabling next generation logistics and planning for smarter societies’, Procedia Computer Science, 109, pp. 1122-1127.

Tao, F. et al. (2018) ‘Data-driven smart manufacturing’, Journal of Manufacturing Systems, 48, pp. 157-169.

Tiwari, S., Wee, H. M. and Daryanto, Y. (2018) ‘Big data analytics in supply chain management between 2010 and 2016: insights to industries’, Computers & Industrial Engineering, 115, pp. 319-330.

Wamba, S. F. et al. (2018) ‘Big data analytics in operations and supply chain management’, Annals of Operations Research, 270(1-2), pp. 1-4.

Wang, G. et al. (2016) ‘Big data analytics in logistics and supply chain management: certain investigations for research and applications’, International Journal of Production Economics, 176, pp. 98-110.

Witkowski, K. (2017) ‘Internet of things, big data, industry 4.0–innovative solutions in logistics and supply chains management’, Procedia Engineering, 182, pp. 763-769.

Yan, Z. et al. (2019) ‘The application of big data analytics in optimizing logistics: a developmental perspective review’, Journal of Data, Information and Management, 1(1-2), pp. 33-43.

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