The Digital Equipment Operator
The Digital Equipment Operator is utilizing AIoT to optimize how he operates physical assets or equipment. Goals often include asset performance optimization and process improvements. Examples for Digital Equipment Operators include manufacturers, electricity grid operators, railroad operators, and mining companies. This section will introduce the concept of the The Digital Equipment Operator in detail, again following the why, what, how structure from the introduction.
WHY
The motivation to become a Digital Equipment Operator can be manyfold. When looking at the total operator budget, one will often find that the budget is split between operations / business support and the total asset costs, including asset related projects and asset maintenance. Managing the complex asset lifecycle from acquisition over operations to decommissioning offers many opportunities for optimization. In addition, physical assets usually support business processes, which will also benefit from digitalization.
Typical pain points related to this include low levels of asset utlization, high capital expenditure, asset-related process inefficiencies and quality issues (including the quality of manufacturing output), as well as revenue loss due to asset downtimes.
WHAT
AIoT can support the Digital Equipment Operator in many ways. Smart, connected solutions can help improving OEE through improved visibility, advanced analytics and forecasting. AIoT can help enable OEMs to support Asset-as-a-Service offerings. For the operator this is interesting because it can help shift from Capex to Opex, making innovation investments easier. Asset Performance Management (APM) aims at taking a holistic view at production as well as maintenance, utilizing data and insights generated via AIoT. Process optimization can play a big role, e.g. to improve manufacturing quality and minize scrap rates. Finally, using predictive and preventive maintenance to minimize downtimes can also play an important role.
Example: Escalator operator (railway company)
The first example we want to look at is a railway company which 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 heterogenous, including products from many different vendors. Reducing downtimes will be important for customer satisfaction. In addition, getting improved insights into the escalator´s operational healh status can also help reducing operations and maintenance costs.
For the railcompany, the escalator monitoring has to work for the entire fleet, and must provide a seamless integration with the facility management operations system. Getting all the different suppliers on board to agree on a common solution will be impossible. The logical consequence is to design a solution which 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. The AI-based sound pattern analysis provides insights into the current state and can even predict the future state of the escalator performance.
Once a problem is identified or forecasted, the train station operations personel or the facility managemnt organization is provided with this information and can take appropriate action.
Example: School bus fleet operator
Another good example for a Digital Equipment Operator is a school bus fleet operator, utilizing AIoT to provide a platform which offers shuttle services for schools. Instead of using a fixed bus network and fixed bus schedule, the service is utilizing AIoT to offer a much more on-demand service to students. Instead of using fixed bus stops, virtual bus stops are introduced which can change during the day, depending on demand. Students are can use a smart phone 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 back-end is utilizing 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 below shows an example how the routes for multiple vehicles can be optimized to support multiple stops on a dynamic route.
Example: Aircraft fleet operations planning using flight path optimizer
HOW
Solution Lifecycle
Considerations for execution and delivery
