For many businesses, getting things from A to B represents how they get paid and make money. For manufacturers this involves making goods and shipping them to customers, or providing parts for other organisations; for retailers, selling goods to consumers involves a mix of different channels. In between, there are a wide range of other companies, distributors and logistics operators involved in managing that physical transfer from one place to another. All these businesses have to add value along the way or they will be removed.
The Internet of Things (IoT) should make this process smarter and provide a great opportunity for all companies across the supply chain to reduce their costs and improve service. Investment is growing too – Accenture found that 72 per cent of industrial companies in Europe are increasing their spend on IoT projects, while Gartner forecasts that 14.2 billion connected things will be in use in 2019, and that the total will reach 25 billion by 2021.
Using sensor data, companies can get insight into where their parts or products are over time, while analysis of that data can show opportunities to improve efficiency or automate processes further. Continuous improvement schemes can use data to find more marginal gains. The data can also be used for security and compliance tracking on sensitive goods and materials such as pharmaceutical products.
However, this promise is still to be realised. The issue is that connecting up all this new data to make it useful is harder than first anticipated. So what are the issues?
Building an accurate picture with machine data, not just IoT data
One of the challenges that most companies have to prepare for is just how much data can come from IoT devices or sensors. Cisco estimates that by the end of 2019, the IoT will generate more than 500 zettabytes per year in data. In the years beyond, that number is expected to grow exponentially, not linearly.
However, device data is not on its own. It sits alongside other applications that are used across the supply chain and within the business. While the IoT data itself can be useful to show statistical data regarding where specific devices are or where products have been shipped to, this does not provide enough context to discover why products have been shipped to a certain location and what happens after they arrive. To get this, you have to take data from other applications such as Enterprise Resource Planning (ERP), operations and service applications.
Each of these apps will be made up of multiple components, each producing data on their own performance and results. This data – commonly called machine data – can be used to provide that critical context into how supply chain and logistics operations are linked to specific customer activities or ordering patterns. Equally, looking at machine data can show how the wider business is performing as the business grows or evolves. Getting this wider insight involves linking up all the logs, metrics and alerts created by company applications with IoT data, then putting all this into context automatically.
Building business insight on IoT data
To make the most of all this data, it’s important to look at how to improve both the logistics side of your operations and the business element as well.
There are three main areas with IoT data can reduce costs:
- Operational cost reduction – analysing your IoT data from items such as engine sensors, environmental monitors and other components can support more efficient predictive maintenance programmes. This can reduce machinery and equipment breakdowns that come with higher repair costs compared to fixing issues earlier.
- Faster, better delivery – As prices for remote tracking sensors fall, companies can use IoT and sensor data to achieve fuller visibility into their delivery cycles. This tracking activity can go across the whole supply chain, showing you how your services fit into wider processes over time.
- Green business – Improving efficiency around climate control and fuel consumption can reduce spending on transportation and logistics while also providing a substantial environmental benefit too.
However, these areas should not be considered in isolation. Instead, looking at wider business processes and opportunities to make an impact can make the most of internal and IoT data together. For example, looking at the effect of marketing campaigns on services and logistics performance can help uncover where an increase in product sales can lead to additional transport costs. By planning ahead and taking this more context-based approach, companies can improve their overall supply chain and logistics operations with data to manage costs more effectively across the whole business.
Alongside this, it’s also important to bear in mind that these sets of data might be useful to others. Making data available and easy to consume can support analysts and other line of business teams with their own decisions, based on making it easier to consume all this data in the first place. For companies like the IoT device manufacturer Samsung SmartThings, this pervasive adoption and use of analytics are essential to the company’s operations – around 95 per cent of all its employees use analytics and dashboards as part of their roles, from software development through to customer service and financial operations.
As we have seen from the Gartner and Accenture statistics above, investment in IoT devices and services will continue to grow. However, these investments have to be coupled with a strong strategy around machine data analytics that brings IoT data closer to people. Without this ability to put data in context, IoT projects will continue to serve niche use cases rather than the needs of whole organisations.