The Digital Equipment Operator utilizes AIoT to optimize how they operate physical assets or equipment. Goals often include asset performance optimization and process improvements. Examples of Digital Equipment Operators include manufacturers, electricity grid operators, railroad operators, and mining companies. This section introduces the concept of the Digital Equipment Operator in detail, again following the why, what, how structure from the Introduction.
The motivation to become a Digital Equipment Operator can be manifold. A good starting point is to look at OEE (Overall Equipment Effectiveness), or whatever the equivalent in the specific industry. OEE measures the performance of an asset compared to its full potential. OEE quantifies the utilization of manufacturing resources (including physical assets, time, and materials) and provides an indication of any gaps between actual and ideal performance. OEE is often calculated based on the following three metrics:
- Availability, e.g., asset up-time
- Performance, e.g., system speed
- Quality, e.g., levels of defects
Depending on the industry and asset category, the detailed calculation of OEE might be different. In manufacturing, it will often include planned vs. actual production hours, machine speed, and scrap rates. Each industry has its own, specific ways of looking at availability, performance rate, and quality rate. For a rail operator, a key performance rate indicator will be passenger miles; for a wind turbine operator, it will be kWh generated; for a mining operator, it will be tonnes of produced ore.
AIoT can support the Digital Equipment Operator in many ways. Smart, connected solutions can help improve OEE through better visibility, advanced analytics, and forecasting. Asset Performance Management (APM) aims at taking a holistic view of asset performance, utilizing data, and insights generated via AIoT. Availability can be improved with AIoT-based predictive, preventive, and prescriptive maintenance. The performance rate can be improved with AIoT-generated insights for process optimization. Quality management can be supported by AIoT-enabled quality control mechanisms, e.g., optical inspection. Advanced analytics and AI can also help improve the quality rate.
Example: Escalator Operator (railway company)
The first example we want to look at is a railway company that is operating escalators at its train stations. For a large railway company, this can mean a fleet of thousands of escalators in a wide geographic range. Most likely, the escalator fleet will be highly heterogeneous, including products from many different vendors. Reducing downtimes will be important for customer satisfaction. In addition, obtaining improved insights into the escalators' operational health status can also help reduce operations and maintenance costs.
For the rail company, escalator monitoring has to work for the entire fleet and must provide seamless integration with the facility management operations system. Getting all of the different suppliers on board to agree on a common solution will be impossible. The logical consequence is to design a solution that can be applied to existing escalators in a retrofit approach, potentially even without support from the escalator vendor/OEM.
A good technical solution in this case is to utilize AI-based sound pattern analysis: sound sensors attached to the escalator provide data which can be analyzed either using an on-site edge node, or centralized in the cloud. AI-based sound pattern analysis provides insights into the current state and can even predict the future state of escalator performance.
Once a problem is identified or forecasted, the train station operations personnel or the facility management organization is provided with this information and can take appropriate action.
Example: School Bus Fleet Operator
Another good example of a Digital Equipment Operator is a school bus fleet operator, utilizing AIoT to provide a platform that offers shuttle services for schools. Instead of using a fixed bus network and fixed bus schedule, the service utilizes AIoT to offer a much more on-demand service to students. Instead of using fixed bus stops, virtual bus stops are introduced that can change during the day, depending on demand. Students can use a smartphone app to request a ride to and from the school. Shuttle buses are equipped with an on-board unit to provide bus tracking and AI-based in-vehicle monitoring. The platform in the backend utilizes AI to optimize the pick-up order and routing of the shuttle buses.
This example will be discussed in more detail in the Sourcing Chapter. The figure following shows an example of how the routes for multiple vehicles can be optimized to support multiple stops on a dynamic route.
Example: Aircraft Fleet Operations Planning using a Flight Path Optimizer
Modern airlines were amongst the first to become Digital Equipment Operators, first utilizing telematics, M2M and now IoT in combination with advanced analytics and today's AI. Managing a large fleet of aircraft is a challenging task. One critical process in this context is flight path planning. The flight path describes the way from one airport to another, including detailed instructions for take-off and landing, as well as the way between the two airports. From the airline´s point of view, the two most important aspects are safety and fuel costs. The latter requires inputs such as weather conditions, overflight fees, fuel costs at the origin and destination, as well as aircraft performance data. Based on this information, the flight path optimizer can calculate the optimal route.
There is no one-size-fits-all answer to becoming a Digital Equipment Operator. This section looks at a generic Solution Lifecycle, as well as considerations for execution and delivery (analogous to the previous section).
For some Digital Equipment Operators, there will be a central AIoT solution that is at the core of their fleet operations. The airline's fleet planning system might be such a core application. However, very often, Digital Equipment Operators will find that the solutions they require are on the long tail of the AIoT chart (see discussion on the long tail in AIoT 101). This means that they are looking at building multiple, specialized solutions, that need to be constantly enhanced and adapted. This can be supported by a measure/analyze/act approach. The AIoT in High-Volume Manufacturing Network case study provides a good example for this approach.
Considerations for Execution and Delivery
For the Digital Equipment Operator, execution and delivery will require a different perspective than for the Digital OEM. While any investment will have to be justified by a matching business case, the overall business models tend to be much more straightforward. Similarly, leadership and organization are important but probably not as challenging. Other aspects, such as sourcing, UX, DevOps, compliance and legality, and productization, will be less important compared to the Digital OEM. Data strategy, on the other hand, will be key, especially if a multitude of potentially heterogeneous data sources -- sensors and enterprise systems -- will have to be integrated. Finally, the Digital Equipment Operator will have to focus on fitting the new AIoT solutions into existing business processes. And in order to get there, asset retrofit, site preparation, and rollout management will be key prerequisites.