The Internet of Things (IoT) is a fast growing field thatcould become the one thing to revolutionize the world of business operations,including supply chain management. Imagine how much more efficient andprofitable a company would be if it had technological capabilities to do suchthings 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 achievegreater production efficiency and customer satisfaction. This may not be adream anymore, as rapid advances in technology have enabled companies aroundthe globe to achieve greater operational efficiency than ever before. Butdespite the promises that such technologies bring, there still exist challengesthat must be overcome first. Below is a brief list and description of four mainissues that are holding IoT back from achieving its true potential.
Data quality and integration
Some of the biggest challenges facing data collectioninclude ensuring that enough good data is collected as well as having theability to integrate data from multiple sources. Data collection devices suchas sensors cost companies but just the devices themselves, but also expensesassociated with installation, maintenance, connectivity, and power. With thetotal costs quickly adding up, implementing good data collection systems can becomea huge financial barrier for many companies. Additionally, some data are justout of the reach due to existing technologies’ limitations and environmentalconstraints. Furthermore, many companies still use legacy systems that do nothave Internet connectivity, making it difficult for data integration.
GE Power & Water is one example of a company that isfacing data quality issues. It is investing heavily into monitoring andalerting systems that use data collected from various sources and in differentforms, such as those generated from customers’ operational data and theirinventories. Although these data are enough for operational improvements, GE isnot satisfied yet with their level of timeliness, completeness, and accuracy.In order to improve this issue, GE is looking for ways to automate the datacollection processes that are currently still being done manually, and to offermanagement better visibility of company data through the use of a data qualityportal that tracks the most pressing problems that the company needs to solve.
Most data are collected, transmitted, and stored viacellular networks that continue to expand an ever increasing coverage area overthe years. However, these networks are mostly still concentrated in and nearbig metropolitan areas. There are still many places where networks – and data –cannot reach. As a result of this, companies are limited in their ability togather data from areas not covered by these networks.
Moreover, network capacity also plays a limiting role on whata company can achieve operationally regardless of its technology. For instance,ConocoPhillips spread its radio towers across Texas to transmit sensor data forthe purpose of optimizing gas and oil well production. However, the issue is thatthe network transmitting all this data cannot handle the amount of data neededfor the company to conduct real-time analysis. As a result, this lack ofsupporting infrastructure impedes the implementation of newer technologies.
Another issue that many companies need to face, whenattempting to incorporate the Internet of Things into their product designs, isthat the network becomes a crucial part of the customer experience andperception of these products. Yet, networks are outside of these companies’controls, effectively turning this into an issue of outsourcing of companybrand from the customer perspective. Unsuccessful use of network can cost acompany more than just the network expenses as a result.
Data integration versus analysis
Data analysis certainly does require expertise fromdata-savvy people in order to generate useful insights for decision makingsupport. However, all of these analyzed data generate limited value if they areisolated and stored in silos, instead of being integrated to provide a biggerpicture for an organization to use. This situation is exactly what a lot of healthcareorganizations are struggling with and trying to fix. For instance, hospitalsoften use different types of data in different departments, but when these arenot used together to provide a complete picture of patients’ health, a lot ofresources are wasted in healthcare delivery inefficiencies such as duplicatetests and medical errors.
Data security inadequacy
Although the information technology (IT) industry has hadnearly two decades of experience in data security, this experience does nottranslate as well to the Internet of Things world of operational technology(OT). Simply adding more IT security does not solve OT problems. Compared to ITdatacenter security, OT involves much more frequent physical maintenance andrepairs of machines, which necessitate that the amount and type of securitymeasures needed be different. An example of this is the monitoring system usedon large power plant turbines that can continuously keep track of theactivities of maintenance crews and decide whether or not these activities posesecurity threats.
Questions: Do you feel that the Internet of Things hasenough potential to generate good ROI for companies despite all of the existingchallenges mentioned above? If not, how long do you feel it will take for all of thenecessary supporting infrastructures to be in place before IoT can start showingits true promise?