Monday, December 1, 2014

What Is Holding The Internet of Things Back

The Internet of Things (IoT) is a fast growing field that could become the one thing to revolutionize the world of business operations, including supply chain management. Imagine how much more efficient and profitable a company would be if it had technological capabilities to do such things as: track demand and supply in real-time, make more accurate forecasts, reduce lead times, manage inventory in a more just-in-time fashion, and achieve greater production efficiency and customer satisfaction. This may not be a dream anymore, as rapid advances in technology have enabled companies around the globe to achieve greater operational efficiency than ever before. But despite the promises that such technologies bring, there still exist challenges that must be overcome first. Below is a brief list and description of four main issues that are holding IoT back from achieving its true potential.

Data quality and integration

Some of the biggest challenges facing data collection include ensuring that enough good data is collected as well as having the ability to integrate data from multiple sources. Data collection devices such as sensors cost companies but just the devices themselves, but also expenses associated with installation, maintenance, connectivity, and power. With the total costs quickly adding up, implementing good data collection systems can become a huge financial barrier for many companies. Additionally, some data are just out of the reach due to existing technologies’ limitations and environmental constraints. Furthermore, many companies still use legacy systems that do not have Internet connectivity, making it difficult for data integration.

GE Power & Water is one example of a company that is facing data quality issues. It is investing heavily into monitoring and alerting systems that use data collected from various sources and in different forms, such as those generated from customers’ operational data and their inventories. Although these data are enough for operational improvements, GE is not satisfied yet with their level of timeliness, completeness, and accuracy. In order to improve this issue, GE is looking for ways to automate the data collection processes that are currently still being done manually, and to offer management better visibility of company data through the use of a data quality portal that tracks the most pressing problems that the company needs to solve.

Network limitations

Most data are collected, transmitted, and stored via cellular networks that continue to expand an ever increasing coverage area over the years. However, these networks are mostly still concentrated in and near big metropolitan areas. There are still many places where networks – and data – cannot reach. As a result of this, companies are limited in their ability to gather data from areas not covered by these networks.

Moreover, network capacity also plays a limiting role on what a company can achieve operationally regardless of its technology. For instance, ConocoPhillips spread its radio towers across Texas to transmit sensor data for the purpose of optimizing gas and oil well production. However, the issue is that the network transmitting all this data cannot handle the amount of data needed for the company to conduct real-time analysis. As a result, this lack of supporting infrastructure impedes the implementation of newer technologies.

Another issue that many companies need to face, when attempting to incorporate the Internet of Things into their product designs, is that the network becomes a crucial part of the customer experience and perception of these products. Yet, networks are outside of these companies’ controls, effectively turning this into an issue of outsourcing of company brand from the customer perspective. Unsuccessful use of network can cost a company more than just the network expenses as a result.

Data integration versus analysis

Data analysis certainly does require expertise from data-savvy people in order to generate useful insights for decision making support. However, all of these analyzed data generate limited value if they are isolated and stored in silos, instead of being integrated to provide a bigger picture for an organization to use. This situation is exactly what a lot of healthcare organizations are struggling with and trying to fix. For instance, hospitals often use different types of data in different departments, but when these are not used together to provide a complete picture of patients’ health, a lot of resources are wasted in healthcare delivery inefficiencies such as duplicate tests and medical errors.

Data security inadequacy

Although the information technology (IT) industry has had nearly two decades of experience in data security, this experience does not translate as well to the Internet of Things world of operational technology (OT). Simply adding more IT security does not solve OT problems. Compared to IT datacenter security, OT involves much more frequent physical maintenance and repairs of machines, which necessitate that the amount and type of security measures needed be different. An example of this is the monitoring system used on large power plant turbines that can continuously keep track of the activities of maintenance crews and decide whether or not these activities pose security threats.

 

Questions: Do you feel that the Internet of Things has enough potential to generate good ROI for companies despite all of the existing challenges mentioned above? If not, how long do you feel it will take for all of the necessary supporting infrastructures to be in place before IoT can start showing its true promise?


 

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