Lean data management for maintenance value

Projektinformationen

LeaD4Value

ID Finanzhilfevereinbarung: 751622

DOI

10.3030/751622

Projekt abgeschlossen

EK-Unterschriftsdatum 29 März 2017

Startdatum 1 September 2017

Enddatum 31 August 2019

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 195 454,80

EU-Beitrag

€ 195 454,80

Koordiniert durch

UNIVERSITY OF SUNDERLAND
United Kingdom

Periodic Reporting for period 1 - LeaD4Value (Lean data management for maintenance value)

Berichtszeitraum: 2017-09-01 bis 2019-08-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

We live in a world being transformed by data and digitalization. Modern, advanced technologies are producing more and more data. This creates vast opportunities for data-based decision making and innovations, however it also causes new kind of challenges. Decision makers are often drowning in the data overload, and are not able to convert the data into valuable knowledge and finally into better decisions.

The “Lean Data management for maintenance Value” project (LeaD4Value) addresses the problem from the perspective of maintenance and asset management. Maintenance is often seen as “necessary evil”, but its importance in keeping engineering and infrastructure assets safe and productive is crucial. Europe is suffering from a significant maintenance backlog because most new manufacturing investments are directed to other continents. This has resulted in not only relatively old equipment and facilities in the private sector, but also in deteriorating public assets, for instance unsafe roads and bridges, as well as schools or hospitals damaged by mould. It is important to find new, smarter ways to produce maintenance services. Data-based maintenance decision making can contribute to this issue, and that is why it is important to develop new managerial tools and methods to support the valuable and resource efficient use of data in maintenance.

The main objective of LeaD4Value was to show how the business value of industrial maintenance can be maximized through adopting data-based decision making. This included studying the existing and potential data exploitation paths and their business value in maintenance organizations, and developing lean maintenance data management processes to prevent data overload. The big data hype has led many organizations into gathering a lot of data without proper plans for using it. The research conducted in this project showed that the value of data should be assessed to ensure that the additional benefits exceed the costs caused by collecting, storing, and analyzing the data.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

In the beginning of the project, a state-of-the-art literature review on existing research on data-based maintenance management was conducted. Next, maintenance managers from the partner organizations (a food manufacturing company and an automotive industry parts manufacturer) were interviewed to identify their current state in relation to the state-of-the-art. Based on these interviews, as well as selected maintenance and production data from the partner organizations, the existing maintenance data exploitation paths used by the companies were mapped, and the potential to increase the business value of maintenance through developing the data exploitation was assessed. To support these analyses, a maturity model for valuable maintenance data management was created based on literature and the case studies with the partner organizations.

The main results include: 1) a value stream mapping –based method to identify waste and assess the leanness and value of data exploitation in maintenance, 2) an analytical Wasted Value of Data (WVD) –model to quantify the value of data-based maintenance investments, and 3) a maturity model for valuable maintenance data management. The results were supported by a number of frameworks and analyses, including for example a framework of maintenance lifecycle data exploitation, and a conceptual framework on the factors influencing the adoption of big data and lean data –based innovations in industrial maintenance.

The managerial tools and methods developed in the project are available for the partner organizations to exploit. In addition, they have been disseminated to the academic and industrial communities through 4 peer-reviewed scientific journal manuscripts, 5 peer-reviewed scientific conference articles, and a non-scientific article to be published in a trade magazine focused on maintenance and asset management. Other dissemination and communication activities in the project include a project website, several press releases, presentations for Bachelor and Master level students, multiple presentations at industrial events and conferences, and presenting some of the results in the form of comics.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Maintenance management is a multidisciplinary topic, requiring understanding of maintenance and reliability engineering but also the managerial side including costs and investments, organizational aspects to optimize performance, as well as data sciences to be able to make evidence-based decisions. However both industry and academia tend to compartmentalize maintenance, which results in a lack of skilled maintenance personnel as well as communication issues between engineers and managers. The results of LeaD4Value contribute to the need of managerial maintenance tools, supporting maintenance engineers and managers in communicating with company management in terms of monetary business value. Including digitalization and the increasing use of data in maintenance into the managerial tools is what makes them truly novel. Existing research has not presented many tangible methods to quantify the value of data or information, even in application areas other than maintenance. Data can be considered as a factor of production, however the methods and logics of managing it as one have not yet been explicitly defined. Most research on the topic of the project has focused on big data analysis, whereas this project addressed the value of data and highlighted lean data management as a viable option to companies who cannot afford spending extensive amount of resources experimenting with big data.

The results of the project support organizations in assessing the profitability of their data-based maintenance processes and decisions. Focusing on the right, valuable data will improve the performance of maintenance, increasing asset availability and safety. This will have a positive impact on the productivity and finally the profitability of the organizations. The managerial tools designed in the project can be applied by organizations providing in-house or outsourced maintenance services. The results can also, at least to some extent, be applied to the decision making in infrastructure asset management in private and public sectors. In addition to the industrial and societal contribution, the project also had a significant impact on the career development and professional international contact networks of the research fellow.

Research problem: challenges of data-based maintenance management