C HAPTER 11
4. Industry Applications
Industry 4.0 has brought the idea of smart factories consist of smart objects that offer integrated processing and communication capabilities. Employee communication and the interaction level of humans and technology were improved since the smart objects had emerged in factories. Autonomous systems have brought new problems in factories; however, their autonomous and self-organizing characteristics made the complex manufacturing systems more controllable and sustainable (Gorecky et al. 2014). The new generation of manufacturing technologies and smart factory concepts are changing the production systems in many ways. Additive manufacturing (3D printing), hybrid machines that are capable of performing multiple jobs, new materials and components, factory automation supported by low-cost robots are transforming the conventional manufacturing systems into the smart manufacturing systems. This new paradigm will also impact transportation which is a vital resource for companies. Transportation can be categorized into two groups, internal and external. While internal transportation includes material handling within a manufacturing system, external transportation covers the supply and distribution network of a company. Advances in robotics and autonomous vehicles expose huge improvement opportunities to internal and external transportation by offering a high level of autonomy and sharing, on a regional and global scale (Kusiak 2018).
Mobile robots are capable of moving around via wheels, tracks or legs. AGVs are wheeled mobile robots that usually operate in a factory. AGVs can operate both in an office environment and heavy industrial surroundings. While most AGVs use sensors to follow guide wires attached on the floor, some types can be programmed to pursue a trajectory and to make decisions on the way using the signals they receive (Gruver 1994). The use of AGVs is a widespread phenomenon among the flexible manufacturing systems that involve transport robots in their manufacturing processes. The paths of AGVs are restricted to predetermined routes by incorporating magnetic stripe navigation or guide wires, and they require the workplace to be restructured for them to work efficiently (Arkin and Murphy 1990). Automobile manufacturer SEAT is one of the companies that intensively utilizes AGVs on its shop floor (Figure). The company reports that 125 AGVs are in use in their Martorell facility in Spain. These robots convey nearly 24,000 parts daily, participating in manufacturing with 7000 employees. AGVs facilitate and optimize the workers’ jobs and lead to an almost 25% reduction in production time (Volkswagenag.com 2019).
Fig. 4: AGVs used in Seat Martorell Facility Spain (Volkswagenag.com 2019).
The birth of Industry 4.0 paradigm led to the development of the Smart Logistics concept. Many warehousing and shipping companies have been taking advantage of information technologies, robotics, and automated systems since they integrated AI into their business models. The rapid development in AI and robotics technologies has offered ground breaking systems for the logistics industry such as unmanned warehouse and delivery drones. Companies expedited shipping time and improved the quality of customer service significantly as a result of adopting the intelligent warehousing and delivery service. Unlike the manufacturing industry, the logistics sector copes with adaptation problem that stems from the variety of orders from many customers. Since the orders in the delivery sector are unique in terms of sorting, packing, and delivering, the technological machines and equipment should be equipped with intelligent features. This technology helps the firms to focus on customization by taking each customer’s requirement into account, and improve the customer service level by delivering the right good in the right place at the right time. Also, large order quantities urge the delivery companies to employ automated and intelligent systems to avoid delay in lead times (Wen et al. 2018).
Many technological devices that ease our everyday life already exist in the human environment. When various kinds of robots will be designed to cooperate with each other to perform our daily tasks in the near future, they will be an indispensable part of human life. Wheeled robots, legged robots, humanoid robots, and network sensors will provide various services to humans by either working autonomously or working together. This cooperation among various robots is beneficial to many human activities such as warehouse management, industrial assembling, military applications, and daily-life tasks. The logistics sector will benefit from the coordination of several mobile robots. Heterogeneous multi robot systems, composed of different types and sizes of robots, already became a vital part of warehouse management systems.
These multi-robot systems consist of many autonomous robots that are capable of communicating with each other via wireless networks, and they are used to transport different objects in warehouses (Wang et al. 2012).
Robots have involved many activities in manufacturing processes since the 1950s. They have been intensively used for repetitive tasks such as cutting, welding, and assembling in the automotive industry. In addition to these repetitive activities, the optimization of the internal material flow of a company can be accomplished using the robotics system in the logistics activities. Some of these activities completed by robotics systems are loading/unloading and palletizing/
depalletizing of goods and materials (Echelmeyer et al. 2008).
E-commerce (aka. electronic commerce or internet commerce) giants such as Alibaba and JD.com receive millions of orders every single day, while these online orders constitute a major problem of delivery. E-commerce companies have to deal with the problems of slow/wrong deliveries, lost packages, damaged goods, and incorrect packing while fulfilling millions of orders placed online each day. This challenge encourages e-commerce companies to integrate automated systems into their distribution network. E-commerce logistics activities comprise of three main stages. Replenishment of the goods from the suppliers to the warehouse or distribution center is the first stage. The fulfillment of the customers’
orders at distribution centers is the second stage, and this stage usually consists of picking, sorting, and packing operations.
Finally, the third stage is the delivery of the orders from the distribution centers to customers. E-commerce companies usually collaborate with 3PL service providers to carry out the first and third stages. The second stage is the source of the bottleneck for e-commerce logistics operations, especially during the peak season. Order picking is an extremely labor- intensive task and it requires human operators to move long distances in a highly limited space for storage and order processing. Companies invest in automated systems and robots to reduce this bottleneck in their warehouse operations.
This endeavor covers both the automation of the flow of materials and the flow of information (Huang et al. 2015).
Drones, commonly known as unmanned aerial vehicles (UAVs), are electronic devices that are capable of sustained flight without any human operator on board. Drones perform useful actions under sufficient control such as the delivery of small items that are urgently needed in areas that are not easily accessible. Drone delivery has been applied to healthcare and humanitarian logistics areas in recent years. For instance, delivery of urgently needed medications, blood, and vaccines at the right time when land transport is challenging due to the poor transportation infrastructure, traffic congestion, or severe
164 Logistics 4.0: Digital Transformation of Supply Chain Management
Fig. 5: Amazon’s Octocopter Drone (left) and The Horsefly Drone Intended to Use by UPS (Bamburry 2015).
natural conditions (e.g., weather or disasters). Drones are useful when human lives are in danger. For instance, drones helped rescue teams to pinpoint the survivors after the Nepal earthquake in 2015 (Scott and Scott 2017).
Cost-saving and high delivery speed are the two main drivers behind the spread of drones used in supply chains.
Commercial drones are still in their early stages of gaining attention; however, they already are considered as disruptive technology that will impact the future of product delivery service. Giant companies such as Amazon, Google, and UPS started to invest in this innovative product delivery method. Amazon named her drone project for its online shopping portal as Amazon Prime Air. The company aims to deliver customer orders in less 30 minutes using UAVs when this technology becomes fully functional. UPS also attempts to adopt drone delivery to improve productivity, reduce fuel costs and accidents at work. Google stepped into the drone business by starting the Project Wing drone program in 2014. The company mainly focuses on delivering first-aid kits, defibrillators, and medical products to a scene of a crisis promptly (Bamburry 2015).
Google’s Project Wing is a drone delivery service aiming to increase access to goods, reduce traffic congestion in cities, and help to reduce the CO2 emissions resulting from the transportation of goods. The project also includes the development of an unmanned traffic management system that will allow UAVs to navigate around other drones, manned aircraft, and other obstacles like trees, buildings and power lines (X – Wing 2019).
5. Conclusion and Future Research
The 21st century has become the era of the digital transformation accompanied by newly emerging technologies. It is not possible to disjoin human life from emerging technologies since these technologies impact every aspect of our lives. Smart factories and smart manufacturing processes have been converting the traditional way of manufacturing into a technology- driven manufacturing approach that utilizes the merits of technology. Digitization of manufacturing changed the way goods are made and delivered while improving the operational efficiency of the manufacturers and making them more profitable. As companies shifted from the linearly organized supply chains to the interconnected supply chain operations, the manufacturing process became more dynamic and controllable.
AI together with the robotics technology are two of the crucial drivers of digital transformation. As the players of manufacturing systems become autonomous and self-driven, the manufacturing efficiency and employee productivity greatly improve. Industrial automation led to the birth of the intelligent warehouse and delivery systems. Many interconnected warehousing technologies that are capable of working together form intelligent warehouse systems. The goods are received, identified, sorted, organized, and prepared for shipment automatically without the need of any human operator. These systems automated the entire operations (from suppliers to the end customer) with minimal cost and error while providing companies a strong market position and competitive edge by increasing customer responsiveness and quality of their service.
References
Arkin, R.C. and R.R. Murphy. 1990. Autonomous navigation in a manufacturing environment. IEEE Transactions on Robotics and Automation, 6(4): 445–454.
Bahrin, M.A.K., M.F. Othman, N.N. Azli and M.F. Talib. 2016. Industry 4.0: A review on industrial automation and robotic.
JurnalTeknologi, 78(6-13): 137–143.
Bamburry, D. 2015. Drones: Designed for product delivery. Design Management Review, 26(1): 40–48.
Bosch Global. 2018. The history of artificial intelligence. [online] Available at: https://www.bosch.com/stories/ history-of- artificial-intelligence/ [Accessed 12 Aug. 2019].
Brady, M. 1985. Artificial intelligence and robotics. Artificial Intelligence, 26(1): 79–121.
Buchanan, B.G. 2005. A (very) brief history of artificial intelligence. Ai Magazine, 26(4): 53–53.
Bush, V. 1945. As we may think. The Atlantic Monthly, 176(1): 101–108.
Datasciencecentral.com. 2017. Top 9 Machine Learning Applications in Real World. [online] Available at: https://www.
datasciencecentral.com/profiles/blogs/top-9-machine-learning-applications-in-real-world [Accessed 15 Sep. 2019].
Deng, L. and D. Yu. 2014. Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3-4):
197–387.
Dopico, M., A. Gomez, D. De la Fuente, N.García, R. Rosillo and J. Puche. 2016. A vision of industry 4.0 from an artificial intelligence point of view. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 407).
The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Echelmeyer, W., A. Kirchheim and E. Wellbrock. 2008. September. Robotics-logistics: Challenges for automation of logistic processes. In 2008 IEEE International Conference on Automation and Logistics (pp. 2099–2103). IEEE.
European Group on Ethics in Science and New Technologies (EGE). 2018. Statement on Artificial Intelligence, Robotics and
‘Autonomous’ Systems. Available at: http://ec.europa.eu/research/ege/pdf/ege_ai_statement_2018.pdf.
Goodfellow, I., Y. Bengio and A. Courville. 2016. Deep learning. MIT press.
Gorecky, D., M. Schmitt, M. Loskyll and D. Zühlke. 2014. July. Human-machine-interaction in the industry 4.0 era. In 2014 12th IEEE international conference on industrial informatics (INDIN) (pp. 289–294). Ieee.
Gruver, W.A. 1994. Intelligent robotics in manufacturing, service, and rehabilitation: An overview. IEEE Transactions on Industrial Electronics, 41(1): 4–11.
Haykin, S. 1998. Neural Networks: A Comprehensive Foundation, Prentice Hall PTR, Upper Saddle River, NJ.
Huang, G.Q., M.Z. Chen and J. Pan. 2015. Robotics in ecommerce logistics. HKIE Transactions, 22(2): 68–77.
Jordan, M.I. and T.M. Mitchell. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349(6245): 255–260.
Kumar, S.L. 2017. State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Engineering Applications of Artificial Intelligence, 65: 294–329.
Kusiak, A. 2018. Smart manufacturing. International Journal of Production Research, 56(1-2): 508–517.
Michie, D. 1968. “Memo” functions and machine learning. Nature, 218(5136): 19.
Mitchell, T.M. 2006. The discipline of machine learning (Vol. 9). Pittsburgh, PA: Carnegie Mellon University, School of Computer Science, Machine Learning Department.
Moor, J. 2006. The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine, 27(4): 87–91.
Pfeiffer, S. 2016. Robots, Industry 4.0 and humans, or why assembly work is more than routine work. Societies, 6(2): 16.
Scott, J. and C. Scott. 2017, January. Drone delivery models for healthcare. In Proceedings of the 50th Hawaii international conference on system sciences.
Selfridge, O.G. 1955. March. Pattern recognition and modern computers. In Proceedings of the March 1–3, 1955, western joint computer conference (pp. 91–93). ACM.
Stanford.edu. 2019. CS221. [online] Available at: https://stanford.edu/~cpiech/cs221/apps/deepBlue.html [Accessed 5 Aug.
2019].
Techopedia.com. 2017. What is the difference between artificial intelligence and neural networks? [online] Available at: https://
www.techopedia.com/2/27888/programming/what-is-the-difference-between-artificial-intelligence-and-neural-networks [Accessed 9 Sep. 2019].
The Scientist Magazine®. 2019. A Primer: Artificial Intelligence Versus Neural Networks. [online] Available at: https://www.
the-scientist.com/magazine-issue/artificial-intelligence-versus-neural-networks-65802 [Accessed 9 Sep. 2019].
Turing, A.M. 1950. Computing machinery and intelligence. Mind 59, 236, pp. 433–460
Volkswagenag.com. 2019. The daily routine of autonomous robots. [online] Available at: https://www.volkswagenag.com/en/
news/stories/2018/01/the-daily-routine-of-autonomous-robots.html [Accessed 24 Oct. 2019].
Wang, T., D.M. Ramik, C. Sabourin and K. Madani. 2012. Intelligent systems for industrial robotics: application in logistic field.
Industrial Robot: An International Journal, 39(3): 251–259.
Warwick, K. 2013. Artificial intelligence: the basics. Routledge.
Wen, J., L. He, and F. Zhu. 2018. Swarm robotics control and communications: Imminent challenges for next generation smart logistics. IEEE Communications Magazine, 56(7): 102–107.
X - Wing. 2019. Retrieved October 21, 2019, from https://x.company/projects/wing/.