These days, sharing information and knowledge is easier than ever before. Knowledge can be pooled together and made available to everyone on Wikipedia, a diverse range of blogs, social media and internal corporate wikis. These new insights are communicated directly and can be accessed from anywhere, at any time. The spiral of knowledge runs through its phases – socialisation (implicit to implicit), externalisation (implicit to explicit), combination (explicit to explicit) and internalisation (explicit to implicit) – much more quickly. As a result, we are always up to date with the latest level of knowledge.
A quick recap: Nonaka and Takeuchi’s spiral of knowledge describes the creation and dissemination of knowledge. Appropriate transformation processes are applied to every individual’s experiences and skills (implicit knowledge) to make these tangible for other persons (explicit knowledge) and, ultimately, to make these accessible to everyone (implicit knowledge).
But what about a machine’s knowledge and experiences? Machines and equipment can also teach us valuable information about how they are used and in which condition they are. If only they could talk to us… It is only recently that your machines have been able to communicate with you – with the help of the innovative application ProductInUse, developed by InUse (formerly OptimData).
As an industrial cognitive space application, ProductInUse creates a space where information is compiled from machines and equipment, classic information management systems (e.g. supply chain, maintenance, installation knowledge etc.) and from interactive communication between machines and people and between different groups of people. The careful analysis of this data produces new insights for the users, which can then be used as a basis for creating targeted digital services, thus opening up a new and promising business area.
Here’s how the InUse concept works:
The ProductInUse application combine data from a wide range of different sources (CRM, ERP, PDM, MES, MOM, …) with data from your machines.
The visualization of time series help you to understand remarkable patterns happening during the lifetime of your machines.
This is where the Studio comes into play, translating the cryptic language of machines into words that we can understand. Machine data is cleaned, transformed and integrated to create a machine language. If a certain pattern is detected in the data, the solution responds to this and reports an event in the Community of Performance.
All sources of information converge in the newsfeed and are monitored by all those involved in the process. This results in a level of shared understanding. The social interaction between people and machines creates collective intelligence; people learn from machines and vice versa. The continuous spiral of the machine’s behaviour, enhanced by experiences from the community, means that new knowledge is constantly being added and internalised.
As a learning system, ProductInUse helps machine and equipment producers to better understand their clients and how their machines are used. This knowledge puts manufacturers in a position to offer their clients additional digital services and to expand their own business models. Such digital services always incorporate the usage and experiences of the respective machines and equipment. The client thus benefits from the optimised performance of their production systems.