A collection of resources and commentary providing an introduction to supply chain management and related systems for students, practitioners, and anyone else interested in learning more about how to design, manufacture, transport, store, deliver, and manage products.
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.
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
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?