In the previous article we learned what IoT is, it’s underlying technologies, and how it needs to be integrated in processes and apps to reap the benefits. The goal of this article is to help understand the various ways in which IoT adds value by providing a framework.
The value of IoT lies beyond simple cost reduction. For example, when considering inventory management, the added value of IoT is increasing transparency, visibility, availability and improving the level of coordination. This in turn enables new business models and unique market positions. In the long term these improvements will be necessary to compete. For example, monitoring the vibration and temperature during shipments in real-time does not save costs, but certain customers are willing to pay for this new level of visibility over their shipments. It is a competitive advantage in the short term, but in the long term it could be required to compete.
IoT does not only provide various types of value, it also has a large variety of solutions. It may seem that applications of IoT are all over the place. From smart fridges in your home to predictive maintenance in trains. And in some way this is definitely true. IoT can be applied in any setting where the physical world has something to tell us. McKinsey (2015) categorizes these settings (contexts) as shown in Figure 1.
Further specifying these settings on company level is useful to identify how IoT can add value. For a production company, important settings would be the factories and production lines. For a transportation company, the vehicles and loading docks. To give an idea, the result of such a specification for a logistics provider is shown in Figure 2. The specification of settings can be much more extensive, depending on the purposes.
The specification of settings in which IoT can be used is the first dimension of the framework. The second dimension is the type of IoT applications. There are six types of IoT applications, in two broad categories: information analysis (1-3), and automation and control (4-6).
|1||Tracking behavior||Monitoring the behaviour of persons, things, or data through space and time. Includes: inventory and supply chain monitoring and management.|
|2||Situational awareness||Achieving real-time awareness of physical environment.|
|3||Sensor-driven analytics||Assisting human decision making through deep analysis and data visualization.|
|4||Process optimization||Automated control of closed (self-contained) systems. Includes: continuous, precise adjustments in manufacturing lines.|
|5||Optimized consumption||Control of consumption to optimize resources use across network.|
|6||Autonomous systems||Automated control in open environments with great uncertainty.|
The type and setting dimensions can be combined in a matrix, as shown in Figure 4. Here settings from the logistics provider example are used. Thinking along these axes helps categorizing the variety of IoT applications, as well as coming up with ideas for your own company. To illustrate, the question for the first cell would be: “what behaviour can be tracked in the driver’s cabin?” One answer could be: whether a driver is alert enough to drive safely through a camera or measuring steering behaviour. Another question: what sensor-driven analytics could be applied for warehouse storage? Answer: the usage rate of individual racks could be used to optimize the storage policy to reduce total travel time, an important KPI in a warehouse.
This framework shows how IoT can add value for companies. Value beyond only the economic aspects: visibility, transparency, and more.
The future is looking bright for IoT
This is the second episode of our IoT-series. In the first article we learned what IoT is. The next and final article will present a roadmap to identify potential and implement IoT in your organization.