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Digital Transformation in the Transportation and Logistics Industry

   

Added on  2023-01-04

11 Pages2532 Words88 Views
Digital transformation in an
industry sector
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INTRODUCTION 150....................................................................................................................3
MAIN BODY...................................................................................................................................3
The impact of digital technologies like Cloud, IoT, Big Data, and AI on the industry.........3
The impact of e-commerce and other digital business models on the industry......................5
Changing forms of customer engagement in the industry......................................................6
Changing organisational cultures in the industry...................................................................7
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................9
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INTRODUCTION
Digital transformation is defined as a set of innovation and transition solution towards new
revenue streams and business model. It is mainly depends upon three key pillars that are
automation, improved manufacturing process and product optimisation (Švarc, Lažnjak and
Dabić, 2020). In order to stay competitive it is important for organisations to digitally transform
their business in order to reduce cost, decentralize production, enhance efficiency and
productivity and create new business opportunity. It also helps to gain more competitive
advantages and stimulate innovation against destruction. Present report discusses the impact of
digital technologies, e-commerce upon transportation and logistics industry. Along with this
report include changing form of consumer engagement and organisational culture upon this
industry.
MAIN BODY
The impact of digital technologies like Cloud, IoT, Big Data, and AI on the industry
There is a rapid technological development in the feel of cloud computing, algorithm
development, big data and processing power on every day basis. These technologies have
significant capability to enhance accessibility, performance and course of business organisation.
With the introduction of new and modern technology such as Big Data, IoT, artificial abd cloud
computing significantly transformed fragmented and unorganised Logistic sector in an effective
manner (Llopis-Albert, Rubio and Valero, 2020). In addition to this it has been identified that
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these technologies are facilitating modification in this industry such as smart roads, predictive
analysis and autonomous vehicles. Furthermore, it has been underlined that artificial intelligence
is significantly helping in optimising the process through which the future opportunities and
challenges can be easily predicted. According to the analysis is has been evaluated that Logistic
is one of the most important industries in which AI is significantly facilitating seamless process
of supply chain management. This technology is becoming an essential aspect of this sector as it
not only reduces the money and time spent on tracking the goods but is also reducing cost and
shipping.
Artificial intelligence is significantly improving Logistic industry and assisting in
network management program through which potential shipping delays. It has been identified
that this technology also helps in predictive risk management of through which organisations
within this sector can effectively monitor, conversations and online news associate in supply
chain factors and can further undertake corrective actions (Dolata, 2020). Artificial intelligence
is playing important role in reducing cost saving and time in handling accuracy and productivity.
Organisations within this sector with the use of this technology can not only save their
unnecessary money and time but it is to assist them in demand forecasting through which cost of
shipping can be effectively reduce (Rof, Bikfalvi and Marquès, 2020). Furthermore, it has been
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