Definition of Artificial Intelligence
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.
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, 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.
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:
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).
Different Type of Application of AI
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 management can make the marketing strategy and procurement as well so as to keep pace with the time.
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.
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.
Chatbots
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.
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.
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).
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.
Machine learning for Supply Chain Planning
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.
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.
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.
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.
Warehouse Management through Machine Learning
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.
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 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.
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 https://www.supplychain247.com/article/transforming_logistics_with_artificial_intelligence
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
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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