A lot of attention is focused on the internet of things (IoT) and its industrial counterpart, the IIoT, but can businesses actually monetize and drive real business value from it? For companies with limited IT budgets and resources, finding that answer can be a challenge.
One way to gauge the value of the IIoT to your business is to initially concentrate IIoT efforts on key performance areas such as asset uptime and maintenance optimization. These functions have used components of IIoT for years, if not decades, in the form of machine and process controllers and monitoring agents that acted largely independently. The IIoT allows such devices to communicate with each other.
Industrial sensors, actuators, and PLC, SCADA, and DCS systems have long been very individualized, custom solutions with a rather narrow application. Simple functions such as tracking cycle counts or when a machine was on or off were possible, but the devices existed in isolation unless some software aggregated the data and provided basic information about operational efficiencies or when maintenance was needed.
The modern technology landscape allows for greater automation, more data from a broader array of devices and sensors, and better ways of correlating and using the larger volume of information. With interconnected software and systems, Big Data analytics, machine learning and other IIoT components, concepts such as unattended operation and prescriptive maintenance become a reality, and new business opportunities open up.
Operations and maintenance improvements
Improving the ability to understand, intervene, adjust, and optimize performance is where the business value and monetization of IIoT takes root. IIoT helps asset owners to understand all the factors that influence their common economic drivers – operational efficiency, operational costs, maximum uptime and minimum unplanned downtime, quality, on-time shipments, etc. – so they can plan and optimize accordingly. Following are some examples:
- IIoT provides insight into the context of an asset’s performance, thus allowing for greater operational efficiency. It is not enough to know that an asset is operating at its intended load. Other factors such as the ambient temperature, humidity level, or excess vibration will influence how it performs. Collecting and correlating both operational and environmental data provides a clearer picture of the machine’s performance and maintenance requirements. Emerging technologies such as machine learning are helping to discover correlations that were otherwise difficult to see.
- With IIoT, organizations can transition out of reactive mode and start predicting maintenance requirements and taking proactive corrective actions. “Prevention is always cheaper than cures and reaction. The more you can predict, and the more accurately you can predict, the better you can prevent an unplanned condition,” says Rick Veague, CTO at IFS North America. Transport operator Sporveien in Oslo, Norway, chose this approach when it equipped its trains with sensors that capture large amounts of data about their service needs, such as door rail bearings requiring lubrication, in order to enable flexible, predictive maintenance, lower costs, and more trains in operation.
- For certain assets, uptime and operational efficiency are improved when they are maintained based on real-life cycle counts. When machines run a lighter duty cycle than expected, the maintenance interval can be safely prolonged, which reduces maintenance costs. Conversely, higher duty cycles may necessitate increasing maintenance frequency to prevent costly unplanned downtime. IIoT allows EAM/CMMS solutions to process non-stop data from this equipment and trigger work orders at the right time and place.
- Location tracking, routing, and logistics optimization are simplified with IIoT connectivity. The movement of raw materials, spare parts, tools and fleets are optimized and costs are lowered when technologies such as sensors, controllers, GPS and RFID tracking devices communicate with one another.
- The IIoT enables system triggers and actionable intelligence, which then generate inventory and supply chain optimization possibilities. Concepts such as replenishment timing, economic order quantities, optimal inventory levels, and balancing supply with demand are more accurate and automated when there is visibility and connectivity across the entire value chain.
- The IIoT is a vital component of today’s digital factories, where production is far more automated and effective than in the past. For example, at Siemens’ Amberg Electronics Plant in Germany, the digital thread ties together all phases of the product lifecycle. Products communicate with production machines and IT systems control and optimize all processes. Production quality is at 99.99885% and the plant’s level of automation is 75%.
- IIoT is a prescriptive maintenance strategy enabler. Prescriptive maintenance uses engineered algorithms and/or machine learning to assess multiple variables and provide higher fidelity for longer range failure prediction and avoidance, explains Ralph Rio, research director at ARC Advisory Group. “It can enable organizations to drive down unscheduled downtime to near zero – with a positive effect on a broad range of KPIs. Case stories indicate that the combination of machine learning with a large number of IIoT sensors provides more advanced notice of a pending failure than traditional, single-variable condition monitoring systems.”