Technology is an Enabler, Information a Resource, but Knowledge is the Prize

The systematic transformation of lifecycle information into knowledge enables exciting new business opportunities that can: assure competitive advantage; achieve sustainability and environmental targets; create new service opportunities; and help to meet regulatory compliance. Closed Loop Lifecycle Management (CL2M) using PROMISE technologies and methodologies is the key.
The rapid development and expansion of Automatic Identification and Data Capture (AIDC) technologies shows no sign of abating. In addition to bar codes, two-dimensional codes, magnetic encoding, electronic codes, and radio frequency identification (RFID), come several networked wireless sensor technologies. To these, add some Feature Extraction technologies – such as biometrics with fingerprint, iris or retinal scanning for humans; and scanning of the unique texture of individual document pages – which may be used for positive identification. Whilst these are all compelling in their own right, they are nevertheless only additional sources of information that can be exploited. They are enablers; they are not the solution itself.

The lifecycle information that can be generated and collected by these myriad technologies is itself additional to the information that may already exist in information systems like CAD/CAM, ERP and CRM systems to name but a few. We discussed the problem of “islands of information” earlier in this series, and recognised that “closing the information loops” is a necessary step in order to consolidate information from many sources, and different phases of life, in order to create a more useful information resource.

However the real advantage, the prize, comes from the ability to take these many different elements of information and transform them into knowledge that enables new insight into product performance and new opportunities for business advantage.
With PROMISE, selected application-specific information is progressively gathered from the chosen sources and stored in the PROMISE Data Knowledge Management (PDKM) system. This process involves one or more data processing algorithms which transform the raw information into knowledge as it is stored. Further algorithms might also be triggered that can add to, or alter, knowledge already in the PDKM.
These may include PROMISE Decision Support System (DSS) algorithms which can further enhance the knowledge, or alert interested parties, about significant events. Alternatively, the DSS system may be used periodically to analyse the accumulated knowledge depending on the specific application requirements.

Taking just one of the PROMISE commercial demonstrators as an example, the collection and analysis of information related to the utilisation pattern of heavy good vehicles can allow different and dynamic maintenance schedules to be applied individually to single vehicles based upon the accumulated knowledge. This in turn can result in optimisation of vehicles by scheduling maintenance only when required rather than at fixed intervals, with consequent reduction in maintenance costs. It can also enable more competitive extended warranty pricing when each truck’s unique utilisation profile can be analysed in comparison to others.

When this same kind of approach is applied to other, quite diverse, applications, such as refrigerators, railway locomotives, passenger cars, engineering machines and heavy earth moving equipment, the advantages are similar – and it is the knowledge that is the prize!

In the next article we will introduce the concept of Lifecycle Events. We will show how they may be used to manage awareness of changes in the life of an entity, and to monitor an entity throughout its entire life.

If you have any comments or questions related to this article, please post them on my blog at cl2m.com.

David Potter is Chief Technical Officer, Promise Innovation International Oy., and former Chairman of the Project Steering Board of the EU PROMISE Project.