You are currently viewing Digitalisation of the Logistics Industry Using IoT, AI, and Cloud

Digitalisation of the Logistics Industry Using IoT, AI, and Cloud

With the rise of the digital economy, companies need to adjust to meet new customer expectations and remain competitive in the market (Freightify, 2021). According to Sniderman & Daecher (2018), the main drivers behind digital transformation in companies are to increase productivity, streamline operations and meet customer expectations. Freightify (2021) explains that embracing digitalisation in the logistics and transportation sector is “no longer a choice” but rather a requirement to ensure future success. 

Technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cloud, have the potential to introduce new efficiencies across the entire supply chain. We discuss how these technologies can be applied to the logistics industry:

Internet of Things (IoT):

Logistics companies have utilised “connected ecosystems” in order to monitor the efficiency of their entire delivery process before the arrival of IoT(Nødskov, 2022). However, IoT has propelled the management of these processes to a whole new level (Nødskov, 2022). 

IoT solutions in fleet management include the installation of sensors and devices in a company’s automobile fleet to gather data about the driver and the vehicle (Kitowska, 2019). This includes the monitoring of wear and tear on vehicle parts, such as the tire pressure, to ensure timely maintenance, as well as the monitoring of driving patterns, such as rapid acceleration, abrupt braking, speed, and inordinate idling. This information can then be used to ensure the driver’s safety, as well as inform the driver on how to conserve fuel and which routes are the most efficient to take (Kitowska, 2019). 

Radio Frequency Identification (RFID) tags are microchips attached to inventory items that wirelessly share information through radio waves to a reader (AR Racking, 2022). RFID tags enabled by IoT technology allow companies to monitor the “status and position” of items within their warehouse (Nødskov, 2022). This improves efficiency within warehouses and reduces “handling errors [and] labour costs” (AR Racking, 2022).

Artificial Intelligence (AI):

AI’s predictive capabilities allow logistics companies to take a proactive approach to forecast future demand (Johnson, 2022). Companies are able to anticipate demand and make data-driven decisions on future stock purchasing and deliveries (Johnson, 2022). AI has also proven invaluable in route optimisation, as it can factor in various variables, such as traffic and accidents, into route planning in real-time to ensure the driver takes the most efficient and safe route to their destination (Johnson, 2022).

Another popular application of AI is robotics in warehouse management (Singh, 2022). These robots perform typical labour-intensive tasks, such as “delivery…storage, picking [and] packing” (Singh, 2022).

Cloud:

Cloud-based applications allow logistics companies to communicate with suppliers, customers, and their teams in real time, allowing quicker issue resolution and increased adaptability to unforeseen situations or disruptions along the supply chain (Matellio Inc, 2022).

Cloud promotes increased visibility into all the processes along the supply chain, while still giving the option to enable “privilege-based access” to conceal any sensitive company data (Matellio Inc, 2022). DFreight (2022) explains that this increased transparency results in better decision-making, as leaders have “real-time data” to base their decisions on. Matellio Inc (2022) further explains that cloud-based software enables “data mobility” across a company’s different departments, resulting in better data management throughout the supply chain.

Cloud-based tools provide increased agility which enables logistics companies to adapt quickly to changes in the market or supply chain (DFreight, 2022).

References:

AR Racking (2022) RFID technology applied in a warehouse and logistics, AR Racking. AR Racking. Available at: https://www.ar-racking.com/en/news-and-blog/storage-solutions/quality-and-security/rfid-technology-applied-in-a-warehouse-and-logistics 

DFreight (2022) Cloud computing-5 best ways to optimize logistics management, DFreight. DFreight. Available at: https://dfreight.org/blog/cloud-computing-benefits-for-logistics-industry/ 

Freightify (2021) 10 reasons why digitalization should be priority for logistics businesses in 2022, Freightify. Freightify. Available at: https://www.freightify.com/blog/10-reasons-for-digitization-2021 

Johnson, M.L. (2022) Top 5 AI applications in Logistics You Need To Know, DC Velocity. DC Velocity. Available at: https://www.dcvelocity.com/blogs/2-one-off-sound-off/post/55159-top-5-ai-applications-in-logistics-you-need-to-know 

Kitowska, K. (2019) 7 IOT use cases in transportation and Logistics, BoostHigh. BoostHigh. Available at: https://boosthigh.com/iot-transportation-and-logistics/ 

Matellio Inc (2022) How cloud technology can benefit logistics industry?, Matellio Inc. Matellio Inc. Available at: https://www.matellio.com/blog/how-cloud-technology-can-benefit-logistics-industry/ 

Nødskov, N. (2022) IoT in Logistics and Transportation, Onomondo. Onomondo. Available at: https://onomondo.com/resource-hub/iot-in-logistics-and-transportation/ 

Singh, B. (2022) Role of artificial intelligence in logistics, Logistics Brew. Stockarea. Available at: https://stockarea.io/blogs/role-of-artificial-intelligence-in-logistics/

Sniderman, B. and Daecher, A. (2018) The innovation paradox, Deloitte Insights. Deloitte. Available at: https://www2.deloitte.com/us/en/insights/focus/industry-4-0/challenges-on-path-to-digital-transformation/innovation-paradox.html 

Leave a Reply