Background of EasyLog and Its Clients
With the rapid changes in the technology and business environment, the logistics industry too currently confronting enormous change. With the change it becomes also obvious to have newer opportunities and risks (Gunasekaran & Ngai, 2014, p.2). New market entrants, newer technology and enhanced customer expectation are also forcing the organizations to adapt with the new changes to sustain in the competitive market.
For this report we are considering “EaseLog”, that provides end to end logistics based solution that provided end to end logistics services for its clients that are distributed throughout Australia and some countries of Oceania. With the total work force strength of 200 people the headquarters of the organization is situated at Sydney.
Clients of “EaseLog” mainly comprises of organizations of warehousing, mining and, manufacturing industry. As the clients are planning to expand their business in next five years through the usage of AI (Artificial intelligence) consequently “EaseLog” is simultaneously planning to use and implement AI based application in order to sustain in the market by meeting their client expectations.
The effect of data driven and independent supply chains gives a chance to leverage the beforehand impossible levels of streamlining the business processes such as warehousing and route optimization (Janjua, Hussain & Chang, 2015, p.41). In order to improve efficiency and conveyance that could turn into a reality in under a large portion of 10 years in spite of high set-up costs preventing early selection of AI in logistics industry.
This report contributes to the discussion on AI, different usage of AI based applications in different industries of Australia and some of the AI based applications that can be used by the “EaseLog”. Furthermore, potential advantages and disadvantages and other considerations from different aspects are also discussed in the different sections of this report.
AI or the Artificial Intelligence utilizes available past data in order to ‘learn’ form it. The available data can contain vast amount of unstructured as well as structured data sets which significantly impacts on the results prompted by the application (Voss, Sebastian & Pahl, 2017). The process of training of the AI agents comprises of providing input of humongous amount of related data instead of writing and compiling millions of lines of code to get the desired output from the application. An efficiently developed and trained AI can expose the hidden patterns as well as predictions depending on the previously acquired data at a speed which helps the business organizations to perform at a great scale (Janjua, Hussain & Chang, 2015, p.45). Compliance check for the acquired data can vary from 5% to 100% sampling which is helpful for internal teams to be more efficient and effective.
AI Applications in Logistics
Machine Learning and Deep learning are considered as the subset of AI. These can be used in wide range of optimisation and prediction experiments by the commercial organizations (Gunasekaran & Ngai, 2014, p.2). This may include defining the likelihood of financial card based transaction being a fraudulent transaction or prediction about an industrial asset to fail in the production line or a customer will leave due to the impact of certain factors.
Deep Learning models the system of neurons for the Ai application in order to separate the features and factors important to a concerned problem, with results enhancing through preparing. Examples of utilization of such uses of AI incorporates robots and self-driving vehicles (Voss, Sebastian & Pahl, 2017).
In order to encourage the coordination and incorporation of SC operations, SC accomplices regularly share data with respect to estimating, joint creation and dispersion arranging through electronic media for example, Internet sites and electronic information exchange. Bounty of such data in the internet gives a prolific ground to applying machine learning systems, for example, web mining and content mining for better decision-making.
As the mining industry contains multiple uncertain factors that have huge impact on the day to day operations and the productivity of the organizations thus with the help of the available geotechnical data to examine ore fragmentation in order to secure the workers inside the mine while maximizing the production from a specific mine (Janjua, Hussain & Chang, 2015, p.41).
For this, the FRAGx algorithm based applications are used by the mining industry, which uses the 3d mapping using cloud data such as 3D Laser Mapping, MVS or UGPSRapidMapper. Using the available data, the application automatically assesses ore fragmentation in few moments.
The FRAGx algorithm are trained in such a way that it spontaneously removes the concrete floors spread over the mine or the shotcrete from the assessment process. More over the results of the examination are unaffected by different natural elements such as wet, dark, humid and dusty underground conditions of the considered mine.
For the manufacturing industry, one of the most recent and critically acclaimed application of AI is “Gakushu”. This is developed by the collaboration of “Nvidia” and “Fanuc” (Japan based robotics organization). The main advantage of this application is fast-tracking the deep learning proces in robots via Nvidia’s GPUs. Gakushu-powered manufacturing robots can learn a manufacturing related task by the inputs from a sensor network that stores data.
Machine Learning and Deep Learning
These robots are able to fine-tune according to the real-time conditions of the manufacturing environment and adjust the process as well as motion. Through this process it can improve 15 percent of the previous cycle-time (Voss, Sebastian & Pahl, 2017).
Use of IBM Watson cognitive computing for order management and Customer engagement by the 1-800-Flowers.com is another example of AI in the retail sector. The “1-800-Flowers.com” is mainly a gourmet food gift retailer organization that operates with more than of its 4,000 employees while delivering the gifts from several brands.
The AI application developed by IBM and utilized by 1-800-Flowers.com analyses the data provided by any client related to a recipient and provides tailored gift recommendations. This is done through the comparison of the facts or details of the recipients provided to gifts booked for alike recipients.
The GWYN or Gifts When You Need feature or experience for the customers can be an alternative to replicate the role of an efficient sales agent at a gift store while providing a detailed and personalized recommendations for them.
GTZconnect for better logistics operations: The GTZconnect AI based application that recognizes patterns in the collected structured and unstructured data. Based on the detected patterns it responds with a recommendation or action for the business process.
Over the time the self-learning ability of the application enables the continuous improvement in the operating algorithms. With the improvement the application becomes able to deliver better and informed suggestions for the logistics operations. In this way the it helps in automation of the logistics decisions while improving the efficiency of related business processes.
The application improves the efficiency by integrating shipper, carrier, user, historical as well as predictive data in order to route inbound inquiries for the logistics operation so that the raised question or issue for the optimization of the processes can be resolved (Gunasekaran & Ngai, 2014, p.4). The application also incorporates real-time data about the weather, traffic on the available routes and other concerned data inputs for recommending the best solution for the desired shipment. In this way it helps the organization to avoid spending of hours on repetitive optimization tasks (Voss, Sebastian & Pahl, 2017). On the other hand, the AI enables the organization and its employees to concentrate on other higher-value business processes.
For the logistics based organization and its clients it is important to aggregate the supply chain related to huge information. In the store network, AI can investigate extensive informational indexes and suggest client administration and activities upgrades while supporting better working resource administration (Janjua, Hussain & Chang, 2015, p.42). As corporate frameworks turn out to be more interconnected, giving access to a more extensive broadness of inventory network information, the chance to use AI improves.
Examples of AI Applications in Warehousing, Manufacturing, and Mining Industries
Logistics organizations like “EaseLog” rely upon networks – both progressively digital and physical which must work amicably in the midst of high volumes, low edges, lean resource assignment, and sensitive deadlines. AI offers organizations the capacity to upgrade to degrees of productivity that cannot be accomplished with human reasoning alone (Rodriguez, Blanco & Gonzalez, 2018, p.680). AI can help the “EaseLog” to rethink the present practices and works on, taking tasks from receptive to proactive, forms from manual to independent, planning from prediction and administrations from standardized to customized.
Use of predictive analysis: For the selected organization “EaseLog”, one of the most progressive application of AI can be AI-produced Predictive analysis, including anticipating request, improving shipping routes and taking care of the complete network of the organization.
With the specific machine learning-based predicting tools it is possible to anticipate cargo travel time delays keeping in mind the end goal to empower proactive moderation of the delay (Gunasekaran & Ngai, 2014, p.4). This predictive analysis can take numerous unique parameters of interior information, the machine learning model can foresee if the normal day by day shipping time for a given path is relied upon to rise or fall up ahead of time in order to take alternative action to mitigate the issues and delay.
While it is considered as difficult to expel AI and fanatically track its status on the Gartner Hype Cycle, the innovation has genuine ramifications for the business, and if outfit suitably, will push ground breaking organizations in front of the opposition (Voss, Sebastian & Pahl, 2017).
Inventory Optimization using AI: For its clients AI additionally assumes a part in the availability of data as the innovation can offer a reasonable value statement to guarantee that the two parties in the logistics process are getting a reasonable arrangement, while likewise observing stock and limit so waver on the execution of the conveyance. The innovation can likewise deal with the provider stock and the routes are accessible for conveyance (Janjua, Hussain & Chang, 2015, p.48). Smart AI algorithms offer this data early to the “EaseLog” know the correct cost and feasible route for the delivery of the certain stock for future conveyance. AI additionally offers information investigation to realize which bearers have moved what cargo in the past at what cost and administration level.
Cost reduction and risk mitigation through AI: Ongoing mechanical leaps forward and expanded requests from shippers have pushed organizations to investigate AI and the arrangements it can offer to its strategic groups. The absolute most basic arrangements that the innovation can offer in the production network are asset administration, cost decrease through lessened redundancies and hazard moderation, supporting conventional measuring methods, accelerating conveyances by upgrading courses, better client administration (Gunasekaran & Ngai, 2014, p.4). With the AI based application business automation, organization can consistently refresh their IT frameworks and improve their information examination procedures to support their calculated procedures.
Advantages and Disadvantages of AI in Logistics
Advantages of using AI in the organization
- Interface and make an interpretation of the collected data to bits of knowledge to empower precise and efficient decision making.
- Improve consistency and deceivability with continuous monitoring of the different factors, propelled process control and end to-end traceability. Reduce wastage of available resources and enhance adaptability at speed and scale the performance of the different processes (Rodriguez, Blanco & Gonzalez, 2018, p.685).
- Improve measures and lessen dependence on coordinate work through self-coordinating machines.
- Rapidly grow new business designs with the deftness to reconfigure the inventory network for new items and new channels.
- Increase cooperation and speed to market, and discharge assets to center around customisation and buyer benefit. Enhance resource execution by remote observing and mechanization of dull undertakings (Janjua, Hussain & Chang, 2015, p.47).
- Improve efficiency with the capacity to work quicker and keep up 24-hour activities
Disadvantages of using AI applications in the organizations
Ethical considerations for the usage of AI in organization
- With the capability of automation of different decision making process can lead to loss of jobs for different type of employees of organization (Rodriguez, Blanco & Gonzalez, 2018, p.683).
- Need to redeploy or retrain representatives to keep them in employments in the organization.
- Reasonable conveyance of resource made by AI applications (Gunasekaran & Ngai, 2014, p.3).
- Impact of machine connection on human conduct and consideration
- Need to wipe out inclination in AI that is made by people in the implementation.
- Security of AI frameworks (Autonomous applications controlling the devices) that can conceivably cause harm (Janjua, Hussain & Chang, 2015, p.43).
- Mitigating against unintended results, as AI based applications are thought to learn and grow autonomously
Conclusion
With the use of the AI, the organizations can streamline large number of business process is now providing competitive advantage to the early adopters by cutting delivery times and expenses in the logistics industry. A cross-industry survey on AI selection directed in mid-2017 by McKinsey found that early adopters with a proactive AI system in the transportation and logistics area delighted in overall revenues more prominent than 5%.
The most important issue which is faced by the organizations is the exponential growth in the generation of different business process related data. This humongous amount of data continues to overwhelm the business organizations and with time this is accelerating. With the adoption of newer digital models for business which are more complex and makes the entire ecosystems of data of processes much complicated. Effective management of the data for the different processes can clearly help the organization in gaining competitive advantage. Where as in case of unmanaged data and AI application implementation may become a barrier to innovation for the organization without the ability to derive significant insight about the business processes.
- In spite of the fact that AI for the most part works best for particular, barely focussed logistical issues on the other hand AI may not work well to deal with vulnerability engaged with cross-utilitarian supply chain and logistics choice situations because of its learning obtaining bottlenecks.
- As AI does not have choice and hence depends vigorously on the programming and training dataset, which may some time lead to wrong choices, on the off chance that it is modified;
- AI based applications may not be anything but difficult to execute on the grounds that they are so elusive and troublesome for conventional decision makers;
- AI can be incorporated with existing frameworks of different operations to complete them without disturbing data streams over the SC.
- Numerous agents based frameworks that can oversee intricacy better can be connected to another set of vital SC issues, for example, SC mix and administration.
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