Internet of Things (IoT) technology has the potential to change many industries and processes as it introduces new cost-effective capabilities for sensing and control. One of the main advantages of IoT technology is that it enables monitoring with much higher granularity compared to legacy systems, from more locations and with shorter sensing intervals. As by definition, data from IoT sensors are available in the cloud, the use of the collected data is simple and does not incur additional costs, thereby offering potentially an attractive ROI for a monitoring solution.
Fundamentally, IoT does not enable the monitoring of new modalities (i.e., physical parameters) as the sensors used in IoT solutions are the same commercially available sensors that we have been using for decades. However, as the IoT technology is relatively low-cost and sensors can be battery-powered, the sensors can be installed in locations and quantities that were not possible before, thereby enabling Big Data opportunities for physical world sensing scenarios.
In logistics, the possible use cases are the monitoring of the goods themselves, and the monitoring of storage areas (both mobile, such as trucks and airplanes, as well as stationary, such as warehouses and display cases). In addition to the monitoring of goods, also the equipment moving the goods can be monitored for location, wear, and condition. The possible modalities that can be monitored are broad, starting from basic environmental conditions, such as temperature or humidity, to more advanced sensing, such as CO2 and shock levels. Additionally, the location of humans, equipment, and goods can be monitored with high granularity, for example, enabling the creation of virtual perimeters outdoors and inside buildings, generating alerts when a person or a box moves into or out of an area.
Oftentimes, organizations attempt to adopt IoT into the existing processes that are already in place and that have been implemented with humans in mind. This may not be the optimal approach as adding technical sensing capabilities usually also calls for changes in processes. For example, when food temperature checks at receiving stations are replaced with automated food condition checks during transportation, the penalties to suppliers or logistics companies can be implemented automatically when required conditions are not met.
Integrating IoT sensing into processes will, in most cases, also require changes in other business software layers to make the best use of the data collected by IoT sensors. In many cases, implementing IoT eliminates the human factor in some process steps but adds human involvement in others. For example, instead of weekly equipment checks by a technician, a coordination activity may be introduced whereby the technicians are dispatched to individual sites, and the priorities being based on automated reports, the severity of the issue, and the customer type.
The above also means that adding IoT sensing may create new processes in an organization when alerts from IoT sensors must be addressed by either humans or machines. For example, the alerts that need to be addressed may be the unacceptable environmental conditions for certain goods, excess vibrations of equipment or a tag being outside of the permitted area. However, as one can understand, such new processes improve service quality or reduce total costs, so these additional processes help to improve the bottom line, thereby generating a positive ROI.
While implementing IoT sensing in an organization generates many opportunities, one should approach these projects as any other business software (or technology) implementation project – by evaluating the impact on the organization as a whole, and the processes already in place within the organization. IoT sensing can be adopted successfully with a holistic approach, including the people, the technology, the existing processes, and the potential changes suggested by the introduction of IoT sensing.
Article author: Jürgo Preden, Thinnect