Artificial Intelligence in Logistics Industry

This report analyzes the use of Artificial Intelligence in the logistics industry, its different applications, advantages, and disadvantages. It also discusses how AI can help in decision making and business planning.

Artificial Intelligence
Unit code: COIT20249
Name: PROFESSIONAL SKILLS IN INFORMATION AND COMUNICATION
TECHNOLOGY
Assessment number: ASSIGNMENT 3
Report title: ARTIFICIAL INTELIIGENCE
Assessment due date: 21/09/2018 (11:55 pm)
Word count (actual):
Student name: MOHAMMED ABDUL ATIF
Student number: 12064061
CQU email address: [e-mail]
Campus lecturer/tutor: IBRAHIM ABDULLA
Unit Coordinator: RUCHIRA DE SILVA
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Executive Summary:
Purpose of this report is to analyze the use of the Artificial intelligence in the logistics industry.
What is the artificial intelligence and how it can help the logistics company in performing their
day to day operation. It also makes the predictive analysis as well for the business planning and
decision making. AI is crucial in today’s technological era because all the decision of the
management must be supported through some data and pattern. Now no one can or wants to take
the decisions blindly instead of that everyone wants it to be supported by some data from the real
market trends. As you know we are moving ahead with our business growth and to keep this
growth momentum intact, I want your attention for the implementation of the AI application in
the logistics company and the way in which it is a very good idea to invest on the artificial
intelligence. In this report, I have tried to explain different type of application which can be used
in our company and how each application can leverage value to our organization and I also did
the analysis regarding the negative and positive aspect of each application. For example, if we
have chatbot installed in our website then definitely it can handle user's query 24*7 and responds
as per their query and if a bot can handle the query then it can suggest how to proceed further
and this kind of automation is the real advantage for the organizations.
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Table of Contents
Executive Summary.........................................................................................................................2
1. Introduction..................................................................................................................................4
2. Definition of Artificial intelligence.............................................................................................4
3. Different type of application AI Applications.............................................................................4
3.1 Chat bots................................................................................................................................5
3.2 Machine learning for supply chain planning..........................................................................5
3.3 warehouse management through the machine learning.........................................................5
3.4- Data cleansing through natural language processing............................................................6
3.5 Self-driving and autonomous vehicle....................................................................................6
4. Advantage and disadvantage of various application of AI..........................................................6
4.1 Positive and negative aspect of chatbot application...............................................................6
4.2 Positive and negative aspect of the machine learning for supply chain planning..................7
4.3 Positive and negative aspect of the warehouse management through machine learning.......7
4.4 Positive and negative aspect Data cleansing through Natural language processing..............8
4.4 Positive and negative aspect Self-driving and autonomous vehicle......................................8
Recommendation.............................................................................................................................8
Conclusion.......................................................................................................................................9
References......................................................................................................................................10
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1. Introduction
We are the logistics company with over 200 staff and have offices all over Australia having head
office in Sydney and also operate in the other countries as well. As we all are seeing the
immense growth in the field of the computer technology and in the field of artificial intelligence.
A volume of the data is also growing yearly and as per our plan for the projected growth in the
next five year, we can say that amount of total data we are getting today will be increased by
two-fold. As we are also planning to expand our offices and freight as well hence our other lots
of things will be increasing like the vehicle, enterprise clients. Communication with clients and
end user is also going to increase immensely (Supply chain Dive, 2018). So for us, it is very
much required to look into the technology perspective, especially Artificial intelligence segment.
Here we can apply and maximize the value of our data by deep learning, augmented learning and
decision making as well as to provide effective customer support as well. This report is about
the analysis of the current trend in the logistics industry and usage of Artificial intelligence in the
field of the logistics industry and how it can help this industry to make sure that they are using
all the data from operation for the business intelligence and effective decision making. AI can be
used for our organization’s day to day operation like customer support, vehicle tracking, and
automation of the business process as well as automatic scanning of the items to make sure that it
is not damaged. These activities using the Artificial intelligence and machine learning will help
to achieve our goal and fast and efficient way.
2. Definition of Artificial intelligence
Artificial intelligence is for the computer technology which can be defined as human intelligence
demonstrated by the machines. Artificial intelligence is the system which mimics, automate and
replicate the human intelligence by continuous learning through the provided data and even
calculating the result faster than a human being. Various definition of the Artificial intelligence
has been till now in nutshell. All the definition is limited to, a computer with processing power
and having right software can make a decision as a human can do and it can interact with the
other actor of the system and automatically and generate the ideas a human being can generate
(Medium, 2018). All the analysis or result of the artificial intelligence is driven through the data,
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do AI system need to have the continuous data from all the sources and its par and process it and
generate the intelligence over the received data.
3. Different type of application of AI
There is various type of AI application which can be utilized in the field of the logistics,
manufacturing, and mining and these applications will make the task very easy and effective in
terms of the time consumed by doing it manually. Some of the applications are:
3.1 Chat bots
Chatbots powered by the artificial intelligence is the perfect example of the instant chat support
without the intervention of the human being. Chatbots are supposed to be powered by the data
and based on the data result they can provide the customer support is a very effective way. The
end user can interact with their queries and bot with the efficient information will reply to the
end user within a second. If chatbot is not able to understand the end user concerns then
customer support can be forwarded to the human support. This will minimize the workload on
the human support team and human support team will be able to focus on the real and complex
issues instead of indulging with repeated and same kind of customer support activities. The more
complex virtual assistant program can be designed to support the complex human queries and
provide the appropriate solution for that (Russell & Norvig, 2016).
3.2 Machine learning for supply chain planning
Machine learning is technology which comes within the scope of the artificial intelligence. It
can be combined with the artificial intelligence to keep learning about the system and generating
the learning outcome to the company. Machine learning can be used to generate the past trend
regarding the inventory of the company and can forecast about the future need. It will help the
organization in the planning of the activities in the advance. Machine learning is the twin of
artificial intelligence technologically and provides immense possibility for generating the
intelligence and calculation done by a human so that management of the company can rely on the
pattern shown by the machine learning application for the forecasting. We need to provide the
right kind of the data for the machine learning to obtain the maximum output and efficient
planning from this and result will help in the decision making and assessing the trend of the
demand and supply in the coming future (Kormushev, Calinon & Caldwell, 2013). So in this way
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management can make the marketing strategy and procurement as well so as to keep pace with
the time.
3.3 warehouse management through the machine learning
As we know warehouse is a very common part of any logistics company. So we must use the
machine learning and artificial intelligence for the warehouse management. Warehouse
management can easily calculate which item is in most demand and is not. In warehouse
management machine learning application will also keep track of the damaged product. It can
forecast the damaged product in future and warn the management about the mismanaging the
item in the warehouse. Based on the current data trend which is being fed into the system and
with other data like season and festival, machine learning can do the predictive analysis to
forecast how much inventory will be needed in the warehouse. Instead overstocking or under-
stocking the warehouse company can plan accordingly to fill the warehouse and so that it can
meet the end user expectation and management will also be able to use the warehouse
appropriately (Houben, Stallkamp, Salmen, Schlipsing & Igel, 2013). "In some judgmental tasks,
information that could serve to supplement or correct the heuristic is not neglected or
underweighted, but simply lacking". If we feed the data of all the order item logistics company
receive then to minimize the damaged products as well.
3.4- Data cleansing through natural language processing
Data which comes to our companies used to be in different languages so we want applications
which can use natural language processing generally called as NLP for converting data from any
language to the homogeneous language which will be understandable to the machine learning
and artificial intelligence application (Ding, Xu & Nie, 2014). "Every-day natural language
communications is much harder. This harder problem has two parts. The first part is to identify
the intended meaning of the communication"(Kowalski, 2011). So NLP can be used for the data
sanitization or cleansing so that it can be ensured that our application is having only the
meaningful data point which can be used for the making the pattern. Based on the pattern
management will be able to analyze and set the strategy to meet the organizational goal.
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3.5 Self-driving and autonomous vehicle
Due to the invention of the autonomous vehicle and heavy research in this field demonstrates
that this is future of the logistics company. Autonomous vehicle will empower the logistics
companies to make sure that all the orders reaching out in the time. Based on the characteristic of
the autonomous vehicle effective time of transportation can be calculated and the deliveries can
be planned in the fast and effective manner. If driverless delivery vans or the trucks coming into
the market then definitely it reduces the human labor cost. It overcomes the other hurdles
associated with driving a vehicle by the human (Sharma, 2018). With the heavy growth in the e-
commerce sector, B2C and B2B, importance of Logistics Company is getting increased day by
day. Most of the companies are looking for the fast delivery to make sure that customer
satisfaction is achieved. For this kind of market, autonomous vehicles are very prominent area of
the Artificial intelligence in the Logistics domain.
4. Advantage and disadvantage of various application of AI
Every good thing comes with the negative side also. AI and machine learning are having lots of
good things but at that same time, they also have some negative impact as well. Below is given
the positive and negative side of all the above-discussed application.
4.1 Positive and negative aspect of chatbot application
The positive aspect of the chatbot application: chatbot is a very good alternative for the customer
support as it provides answer in a predefined way. For the repetitive question, chatbot is a very
good option. Chatbot can read user questions and if it has any such question in the past or has the
prefixed template then it will reply it is very fast and which will be beneficial for the end user
and our organization as well.
The negative aspect of chatbot application: As we know chat bot work based on the artificial
intelligence and machine learning. Chat may replay based on some prefixed templates to respond
to the user. So if a user does not use the proper language or set of defined question pattern then
chatbot might not able to answer at that point of the human intervention is needed to resolve the
customer query. So it is eminent that we cannot fully rely on the chatbot only. We need to have
the alternative solution in place to provide the effective support to the customers (Joseph, 2018).
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4.2 Positive and negative aspect of the machine learning for supply chain planning
Positive aspect: supply chain planning is very effective in term of planning the inventory of the
organization. Some month of the year is very hectic and need some special arrangement to meet
the demand in the market. So though the machine learning if the management is having the data
and pattern in the advance then it can help to make the strategy or planning to make sure that
organization is able to handle the peak demand (Bostrom & Yudkowsky, 2014). If there is any
down season predicted by the machine learning application then definitely management can
think about the underutilized inventory and make some plan to utilize them in the most effective
way so that it can make sure that inventory is managed properly to make most out of it.
Negative aspect: Machine learning and artificial is totally based on the data collection methods
and data processing layer of the complete system (Columbus, 2018). So if there is any mistake in
the data collection then it can hamper the complete learning process and put the management in
the dilemma. Quality of data also matters in the machine learning and data always used to be in
the text, image, audio and video and unstructured data are very tough to mine and generate the
intelligence over it, This may be a challenge for but if we implement it properly then it can have
a very good result.
4.3 Positive and negative aspect of the warehouse management through machine learning
Positive aspect: Based on the trends of the machine learning, warehouse activities can be
planned. If the trend is showing that there is less demand is about to come in the future then its
stock can be reduced is high demand is about to come then inventory in the warehouse can be
increased. So this kind of application will help too for efficient usage of the warehouse space.
Negative aspect: Negative aspect of these kinds of machine learning application is that it needs
data from the multiple sources to derive some pattern or the conclusion. The criterion used to
select the data set collection (which is usually reduced) may bias the comparison results (Barro,
Amorim, 2014). So it will become very messy is any of the data is not coming properly into the
system. These kinds of a system fail to predict the patterns if some unusual event occurs in
meantime. It does not any data point regarding this kind of circumstances.
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4.4 Positive and negative aspect Data cleansing through Natural language processing
Positive aspect: Natural language processing application very useful for processing the voice
data of all the language and converting it to computer understandable language so in this
application all the languages can be processed and based on the data of all the language about
any topic, machine learning or artificial intelligence operation can be performed to make sure
that all the result generated by then are correct and useful to the organization. This application
does not need the people to know all that languages, now application can help the decoding of
the language and making the learning out of it and generating a report.
Negative aspect: Sometimes natural language processing application is very much dependent on
the accent of the user or voice and if the accent is not understood by the application then that
voice is waste for them and thus impacting the application as a whole.
4.5 Positive and negative aspect Self-driving and autonomous vehicle
Positive aspect: This is a very fancy concept and all the big companies are investing on this, so
we can expect that something good can happen with the autonomous vehicle. If autonomous
vehicle come in the market then definitely it is very good for the logistics company and it can the
consignment with the constant speed and do not need any human intervention. This will avoid
any human-made error and make sure that it reaches a destination on time (Scherer, 2015). So
this autonomous vehicle must be having some GPS attached with it and so location and
movement of these vehicles can be tracked live and if something bad happens with then instantly
can be notified to a centralized server for help.
Negative aspect: There are many aspects which can hamper this autonomous vehicle. Driving on
the road is only dependent on a single vehicle only. If someone else is doing the rash driving
then autonomous vehicle might get confused and autonomous vehicle can get damaged due to
others (Suuply chain 247, 2018). It might not able to move smoothly on the road. So in this, it
might take unusual time and hamper the delivery process.
Recommendation
It is recommended that at least we all should look onto all the available artificial intelligence
options which we can include in our organization. I strongly recommend the use of chatbot
because this chatbot will save the time of our support representative. Repeated queries can be
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solved by the chatbot itself. IF user needs some information support then it can be easily
provided by the chatbot. It is strongly recommended to use the Machine learning for the supply
chain management because it will help us in predicting the future supply and demand based on
the available inventory in the system (Barro & Amorim, 2014). It will help the management to
plan the complete process accordingly to meet the end user need. It is also recommended to use
the Warehouse management based on the artificial intelligence. It will provide the deep
understanding and insight of the warehouse. It can predict the trend of how efficient our
warehouse management it and what it needs to improve.
.
Conclusion
Overall we can say that artificial intelligence and machine learning is making a huge impact in
any industry which is having the huge data. Artificial intelligence and machine learning are the
technology which analyzes the data and gives the meaningful data out of bulk. The result from
the artificial intelligence and machine learning can be used for the future strategy making and
streamlining the process to make sure that we have the sustainable growth plan. It can be used
with warehouse and supply chain. Use of artificial intelligence in these sectors has both negative
and positive aspect. At the same time we are not impacting the humans and just using it for our
financial and customer satisfaction growth. It will reduce the efforts of the person at the
workplace. This is essential for the growth of the organization in the long term as it the future of
every industry. At the same time there is also growth in the technology which helps new
generation to use AI. It can create some different improvements to the technology as we saw in
the recent technology example chatbots. This is very essential in the case of B2B and B2C
communication as it is provide 24 hours service to the clients. Artificial intelligence is going to
make the vehicles smarter. There are both positive and negative aspects associated with these
smart vehicles. Artificial intelligence helps in Data cleansing through Natural language
processing which will help in making better decisions. This also has positive and negative
aspects which need to be properly addressed.
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References
Barro, S. & Amorim, D. (2014). Do we Need Hundreds of Classifiers to Solve Real World
Classification Problems?. Journal of Machine Learning Research 15 (2014) 3133-3181.
Columbus L (2018). 10 Ways Machine Learning Is Revolutionizing Supply Chain Management
Retrieved from https://www.forbes.com/sites/louiscolumbus/2018/06/11/10-ways-
machine-learning-is-revolutionizing-supply-chain-management/#136a39813e37
Joseph, T. (2018) How AI is Reshaping the Supply-Chain and Logistics Industry | Fingent Blog.
(2018). Retrieved from https://www.fingent.com/blog/ai-reshaping-supply-chain-
logistics-industry
Kowalski, R. (2011). Artificial Intelligence and Human Thinking. Retrieved from
https://www.ijcai.org/Proceedings/11/Papers/013.pdf
Medium, (2018). 6 Applications of Artificial Intelligence for your Supply Chain. Retrieved from
https://medium.com/@KodiakRating/6-applications-of-artificial-intelligence-for-your-
supply-chain-b82e1e7400c8
Sharma, P. (2018). The Role of Artificial Intelligence (AI) in Logistics - ShipRocket. Retrieved
from https://www.shiprocket.in/blog/artificial-intelligence-role-in-logistics/
Supply chain Dive, (2018).3 ways AI will upend logistics. Retrieved from
https://www.supplychaindive.com/news/artificial-intelligence-disrupt-logistics/521655/
Suuply chain 247, (2018). Transforming Logistics with Artificial Intelligence - Supply Chain
24/7. Retrieved from
http://www.supplychain247.com/article/transforming_logistics_with_artificial_intelligen
ce
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson
Education Limited,.
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Kormushev, P., Calinon, S., & Caldwell, D. G. (2013). Reinforcement learning in robotics:
Applications and real-world challenges. Robotics, 2(3), 122-148.
Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., & Igel, C. (2013, August). Detection of
traffic signs in real-world images: The German Traffic Sign Detection Benchmark. In
Neural Networks (IJCNN), The 2013 International Joint Conference on (pp. 1-8). IEEE.
Ding, S., Xu, X., & Nie, R. (2014). Extreme learning machine and its applications. Neural
Computing and Applications, 25(3-4), 549-556.
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. The Cambridge
handbook of artificial intelligence, 316, 334.
Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges,
competencies, and strategies. Harv. JL & Tech., 29, 353.
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