Internet of Things in logistics

We describe a smart way to apply Internet of Things (IoT) and wireless sensor networks (WSN) in logistics. Especially in the temperature controlled supply chain (cold chain), perishable goods like fruits and pharmaceuticals greatly benefit from real-time quality monitoring during storage and transport in order to avoid quality degradation and spoilage.

Sensing and tracking capabilities of IoT offer special benefits to supply chain (cold chain), especially for perishable goods like fruits and pharmaceuticals. A real-time quality monitoring during storage and transport helps reducing quality degradation and spoilage. Smart sensor nodes track all activities, and check for errors that might occur in the process of handling and distributing goods. The nodes will be programmed to warn when errors occur, and keep an activity record of the entire process. The sensors attached as close as possible to the goods can calculate the remaining shelf life of the perishable goods. Complete monitoring of the entire distribution process also creates the possibility to record and log all activity of relevance in conjunction with environmental data. When products are delivered in a damaged condition, the cause and responsible party can be identified by merely backtracking logged data.

The transport and logistics processes differ dependent on the type of product, its physical shape, climate requirements, etc. But basically it involves the transportation of goods from a factory or supplier to the end customer. In many cases, the actual products are placed on or inside Returnable Transport Items (RTIs), such as pallets, carts, containers, or trailers. Due to the fact that the entire process relies on a number of activities performed by employees, it is common for faults to occur. The barcode, for example, needs to be scanned at several stages. Keeping track of the status of a certain order is carried out by means of the scanning activities, which is vulnerable to faults. Improving the distribution process requires a thorough analysis of the problems that presently occur. The most frequent errors are currently caused because of associating wrong goods to orders, improper environmental conditions for the products, erroneous, ad-hoc placement and misplacement of pallets, carts, and containers on the expedition floor, delays during order picking, transport, and gathering products, wrong placement in trailers, not returning RTIs.

Many of the current problems occur as a result of incorrect handling of the RTIs at various stages of the distribution process. Internet of Things opens up opportunities for other improvements to the process. For instance RTI-order association can be automated. The scanning activity is removed from the process thus reducing the employee’s workload, and position and state of RTIs can be maintained near real-time. Placement of RTIs within cells of the expedition floor can be monitored and guided by the system, and immediate action can be taken if an RTI is positioned in a wrong cell. The system can be used to dynamically allocate RTIs to the grid. As a result, allocated space for the expedition floor can be reduced and usage of cells increased. Correct trailer loading can be verified to make sure that all and only the correct carts are loaded, and positioning within trailer is correct according to the sequence in which RTIs will be unloaded at the retail stores.

To conclude, we discussed the use of wireless sensor network technologies that enable the efficient and effective monitoring of perishable goods in the supply chain. This is an example where a system wide approach in which wireless communication, sensor fusion, data processing, and system architecture interact efficiently to handle the complexity in the logistic supply chain.

Further reading

Bijwaard, D.J.A. and van Kleunen, W.A.P. and Havinga, P.J.M. and Kleiboer, L. and Bijl, M.J.J. (2011) Industry: Using dynamic WSNs in smart logistics for fruits and pharmacy. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys 2011, 1-4 Nov 2011, Seatle, WA, USA. pp. 218-231. ACM. ISBN 978-1-4503-0718-5

Evers, L. and Bijl, M.J.J. and Marin-Perianu, M. and Marin-Perianu, R.S. and Havinga, P.J.M. (2005) Wireless Sensor Networks and Beyond: A Case Study on Transport and Logistics. Technical Report TR-CTIT-05-26, Centre for Telematics and Information Technology University of Twente, Enschede. ISSN 1381-3625

Zhang, Yang and Meratnia, N. and Havinga, P.J.M. (2010) Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 12 (2). pp. 159-170. ISSN 1553-877X

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