Identifying current challenges of data-based maintenance management: a case study

Exabytes of data from various sources are available for maintenance decision makers. The yearly increase in data is exponential due to technological developments such as the rapid increase in the amount of interconnected systems and assets, which utilize smart sensors, cloud-based computing and eMaintenance. All of these are supported by the rapid developments in the Internet. The data provide vast possibilities for smart, autonomous assets and predictive maintenance. However, in practice, there are technical, managerial, and organizational challenges, which impede the maintenance decision makers from exploiting the information retrieved from the data analyses. The existing literature has discussed the data required in different maintenance decision making situations extensively, although there is a limited number of academic publications which explore general-level frameworks or tools to support the management of maintenance data. This paper builds upon a review of the current literature on the value of maintenance data management. The data needed to support a number of different maintenance management situations are discussed, and an approach to analyze and increase the value and resource efficiency of the maintenance data management process is suggested. The paper presents a case study example conducted in collaboration with a UK manufacturing industry. The objective of the paper is to map the current state of maintenance data exploitation paths. This makes the different value-based development needs in the data management process visible. The results of this paper will contribute to future empirical research including modelling and optimizing the use of data in maintenance decision making through adopting lean management principles. The majority of previous lean management research has focused on the optimal management of production processes and the maintenance processes. In this research, the principles of lean management will be taken to the level of optimizing the maintenance data management process

Implementing a CMMS: an investment appraisal based on value of data

Optimal asset data management would require assessing the value of the data regarding the technical and business related goals of the organization. However, the value of data, or data as a cost object, has not been extensively researched. This paper presents a case study on evaluating the value of data -based profitability of investing in a Computerized Maintenance Management System (CMMS). The research is conducted in collaboration with a company who manufacture a range of parts for the automotive industry. Currently the company operates without a CMMS, therefore the implementation of the CMMS is included in the analysis as a scenario. The results will measure the time used in asset data management (gathering, transferring, analysing and exploiting the data), and the value of data in asset management decision making under different maintenance strategies

Using the life-cycle model with value thinking for managing an industrial maintenance network

The objective of this article is to create a general life-cycle model for maintenance decision making in different industries at the item level. The need for network-level tools will increase, as inter organizational collaboration is emphasized more and more. Previous life-cycle models have mostly viewed the matter from the perspective of just one company, but our model takes the different members of maintenance networks into account. We have also integrated value thinking with life-cycle accounting, as it is crucial for companies to perceive which elements increase the value of each member in their network. The value-based life-cycle model introduced in this article has been mainly developed to support the future planning of maintenance operations. In addition, it can be designed how additional value can be reached through future maintenance and how this value can be equitably shared between the network partners

納税者の行為と納税者以外の者の行為――重加算税が課せられる行為と共同事業者の行為の対比を契機として――（一）

As companies have recently gotten more interested in utilizing the increasingly gathered data and realizing the potential of data analysis, the ability to upgrade data into value for business has been recognized as an advantage. Companies gain competitive advantage if they are able to benefit from the fleet data that is produced both in and outside the boundaries of the company. Benefits of fleet management are based on the possibility to have access to the massive amounts of asset data that can then be utilized e.g. to gain cost savings and to develop products and services. The ambition of the companies is to create value from fleet data but this requires that different actors in ecosystem are working together for a common goal – to get the most value out of fleet data for the ecosystem. In order that this could be possible, we need a framework to meet the requirements of the fleet life-cycle data utilization. This means that the different actors in the ecosystem need to understand their role in the fleet data refining process in order to promote the value creation from fleet data. The objective of this paper is to develop a framework for knowledge management in order to create value from fleet data in ecosystems. As a result, we present a conceptual framework which helps companies to develop their asset management practices related to the fleet of assets.Jako, że przedsiębiorstwa w ostatnim czasie zaczęły bardziej interesować się wykorzystaniem wzrastającej ilości danych i zdały sobie sprawę z potencjału analizy danych, uznano, że zdolność do przekształcenia danych w wartość dla przedsiębiorstwa jest dla niego korzystne. Przedsiębiorstwa zdobywają przewagę konkurencyjną, jeśli są w stanie wykorzystać dane floty, które są generowane zarówno wewnątrz, jak i na zewnątrz przedsiębiorstwa. Korzyści płynące z zarządzania flotą opierają się na możliwości uzyskania dostępu do ogromnych ilości danych o aktywach, które można następnie wykorzystać np. w celu uzyskania oszczędności a także rozwoju produktów i usług. Celem przedsiębiorstw jest tworzenie wartości z danych floty, ale wymaga to, aby różne podmioty w ekosystemie współpracowały ze sobą we wspólnym celu – aby uzyskać najwyższą wartość z danych floty dla ekosystemu. Aby było to możliwe, potrzebne są ramy, aby spełnić wymagania dotyczące wykorzystania danych o cyklu życia floty. Oznacza to, że różne podmioty w ekosystemie muszą zrozumieć swoją rolę w procesie rafinacji danych floty w promowaniu tworzenia wartości z danych floty. Celem niniejszego artykułu jest opracowanie ram zarządzania wiedzą w tworzeniu wartości z danych floty w ekosystemach. Jako wynik przedstawiono koncepcyjne podstawy, które pomagają przedsiębiorstwom rozwijać praktyki zarządzania aktywami związane z flotą aktywów

Profitable working capital management in industrial maintenance companies

Purpose – The purpose of this paper is to analyze the impact of working capital management on profitability in industrial maintenance service companies. Design/methodology/approach – Analytical modeling has been used as the research method. Finnish industrial maintenance companies have been analyzed on the basis of their financial statements. Findings – We reveal a significant negative correlation between the cycle times of operational working capital and the return on investment of industrial maintenance companies. Light fixed assets and good profitability of the maintenance sector emphasize the importance of working capital management. Large maintenance service companies seem to achieve competitive advantage over small and medium sized maintenance service providers through both fixed assets- and working capital-related economies of scale, and through the fact that large maintenance service providers often focus on providing services mostly for their host companies. Research limitations – The scarcity of large enterprises in the market in question precludes the use of a more extensive sample in the analysis. Practical implications – In the industrial maintenance service business, more attention should be paid to active management of working capital. We can conclude that this holds true especially in large industrial maintenance service enterprises. Originality/value – We contribute to the unexplored perspective of industrial maintenance companies. Previous studies of industrial maintenance companies have not addressed working capital management, which gains more and more attention under the volatile economic circumstances of the present day. Keywords - Industrial maintenance services, Profitability, Working capital, Flexible asset management model Paper type - Research pape

Appeasing company owners through effective working capital management

In the dynamic financial climate of the present day, companies should be aware of the potential of effective working capital management in reacting to market transformations. It is important to include the company owner’s viewpoint in the research perspective. In this paper, we study the connection between working capital management and the return on equity. The managerial and financial perspectives of flexible asset management are integrated through analytical modelling. We conclude that the return on equity can be improved by shortening the cycle time of operational working capital. Both the interest rate of debt and the debt-to-equity ratio are taken into account in order to study the effects of dynamic financial conditions. Changes in the financial conditions could be compensated through effective management of working capital

A maturity model for valuable maintenance data management

The increasing data provide new opportunities for data-based maintenance management. However, decision makers are struggling to harvest optimally value from their data. This paper presents a maturity model to support analysing maintenance data management processes from the value perspective. Maturity models related to data management and business intelligence have been presented before, but they have not addressed the specific features of maintenance, and have not analysed what makes data valuable. The model presented in this paper categorizes value of data into six dimensions, and defines five maturity levels to assess them. A real-life industrial case example is presented to demonstrate the use of the model. The model takes into account that the most advanced data management solutions do not always represent the optimal target level for every situation. For instance many small and medium enterprises would struggle creating enough additional value from big data to make the required investment profitable