An independent IT environment

Airborne Oil & Gas (AOG)

Airborne Oil & Gas (AOG) is the world's first and leading manufacturer of composite pipes for the oil and gas industry. It recently split off from its parent company Airborne International, of which it was still running the legacy IT. In addition, they had the ambition to largely automate the production environment. Landscape assessed the current IT ecosystem and helped developing a clear data strategy.

Having recently split-off from its parent company, AOG is working on an independent IT environment. This includes having ERP software that supports the primary business processes, IT staff and collecting all data necessary to optimise the production environment. At the stage where we entered the company, all of these were in an early stage.

AOG identified that its IT requirements differed from the legacy IT, for instance the existing ERP that didn't sufficiently support the production process. However, AOG could not exactly pinpoint where gaps occured. In addition, AOG has the ambition to largely automate the production hall. However, it was unclear what data was needed in order to reach that stage.

Landscape suggested a two stage approach, in which firstly, we assessed the IT requirements. Next step was to draft a data strategy.

Assessment IT requirements

Firstly, Landscape assessed the application needs for AOG's seven primary business processes. This involved interviewing business stakeholders, critically assessing the stakeholders' IT requirements, mapping the requirements for these processes to IT applications and data sources, and finally advising on how to improve. Having an overview of the organisation's IT requirements and their connection to business processes has proven to be a very effective communication and decision tool.

Using Landscape's analysis and recommendations, AOG has now started projects to setup an own IT department and to procure new software. Landscape is also actively involved in these new projects.

Understanding data assets

Next, Landscape worked on the issue of what data to collect in order to largely automate the production hall. In order to do automate any process, a machine learning algorithm needs to be trained. The training set is a large data set consisting of examples of how machines should and should not act in certain scenarios. Part of this training set was already in place; part was not.

Landscape assessed the primary - existing and future - data assets in the production hall, including temperature sensors and video cameras. For each, we estimated how much data was going to be collected, which was more than was ever possible to store. By determining the level of data granularity needed in order to train a machine learner, we advised on which data should never be thrown away.

Landscape's analysis and recommendations has formed the basis for several programs to improve data practices and to use data more effecitvely.

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