Purpose: One of the leading factors that shape product and service delivery are data collected in databases and other repositories maintained by companies. The transformation of such data into knowledge and wisdom may constitute a new source of income. This paper aims to explore how small/medium-sized enterprises (SMEs) advance their business models (BMs) around data to handle data-driven products and how this contributes to their innovativeness and performance. Design/methodology/approach: To investigate the phenomenon, the as-is BM of a multinational SME was mapped and its limitations were revealed through a qualitative study. The BM canvas was used. Then the data-driven approach was innovated within the facility management (FM) industry, where a high volume of operational and sensor-based data being collected creates added value in terms of new data-based products. Findings: A data-driven business model (DDBM) blueprint for the FM industry that supports the need to complement service-driven operations with the data-driven approach is delivered. Enhanced BM equips a facility manager with additional managerial tools that enable decreasing property utilization costs and opens up new opportunities for generating revenue. This paper drafts the way to evolve from service to data-driven business and point out the attitudes that managers should adopt to promote and implement DDBM. Practical implications: The DDBM constitutes a guideline that supports FM organizations in focusing their activities and resources on generating business value from data and monetizing data-driven products. Originality/value: The research expands knowledge regarding BMs and their evolution. The gap regarding the DDBM innovation within the FM industry is filled.

Purpose: Process-oriented organizations are compelled to be innovative and continuously implement changes to meet customer requirements and gain a competitive advantage in accordance with the business process management approach. In the digital age, organizations develop business process support systems using agile methods and introduce adaptation skills in order to support their core business. Seamlessly integrating both practices into a coherent and practically applicable solution supporting multi-scenario processes infallibly remains a challenge. The paper aims to discuss these issues.Design/methodology/approach: The workshops identifying the key issues in an international capital group that consolidates facility management companies, as well as a related research review, have fueled a two-cycled Canonical Action Research (CAR) study.Findings: The paper extends the current understanding of IT-enabled agility, proposing an adaptively improved infrastructure with a catalog of digital options that facilitates the composition of multi-scenario business processes.Research limitations/implications: The CAR was limited to the members of management board and the managers constituting the focus groups. The agile business process management (ABPM) model proposed focuses on adding novel functionalities/building new processes, leaving functionality lifecycle out-of-scope. ABPM empirical validation is limited to a single business/technical case.Practical implications: Real-life solution for supporting dynamic business processes was delivered, as rigid IT solutions do not support quick assembly of customized business processes with abundant number of scenarios. The infrastructure enables composition of customer-tailored services based on re-usable digital options directory as well as enhances business process discrepancies/software bugs detection, reducing the cost of maintaining IT infrastructure for entire customer portfolio. A number of issues resulting from building IT solutions within a series of isolated projects with no coherent overall development strategy are mitigated.Originality/value: The ABPM acts as a reference model for focusing on developing critical functionalities and limiting resources consumed by redundant features.

This study investigates data sources for Business Intelligence solutions, which provide a foundation for holistic analyses, optimization, and forecasting of electrical power consumption within Facility Management for retail facilities networks. A multiple case study among Polish subsidiaries of Sweden, France, and UK-based companies along with a domestic Polish has been conducted. The authors identify, verify, and organize the determinants augmenting an energy efficiency-oriented decision-making process and introduce a set of potential benchmarking scenarios for managing it.

Background and Purpose: Business process simulation (BPS) model is based on real-life data form sources like databases, observations and interviews. It acts as “as-is” business scenario can used for reengineering. The main challenge is to gather relevant data and to develop simulation model. Research aims to elaborate BPS model and to systematically assess how close to reality it is. Design/Methodology/Approach: The research has been performed in Polish telecommunications company. Authors investigate technical process of expanding cellular network. After elaborating “as-is” model, authors use ADONIS simulation tool to run a series simulations and confront simulation results with actual historical events. After this, assessment whether computer simulation model can precisely map real-life business process - and consequently act as credible basis for process improvement - is made. Results: The simulation model has been constructed with data from the WfMS database, observations, staff knowledge and their experience. Fully equipped simulation model is found to allow reconstructing the historical execution of business activity with low margin for error. Some limitation were identified and discussed. Conclusion: BPS is not a popular approach for process reengineering and improvement yet. Data collection issues for BPS that require adopting process mining techniques and additional information sources are among the reasons for that. In our study, computer simulation outputs are compatible with historical events. Hence the model reflects the business reality and can be taken as a reference model while redesigning the process.

The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified using the available BLUED data source. The results show that the proposed method guarantees non-intrusive load disaggregation but still requires further research and testing. Measurement data have been published as open research data and listed in the literature section repository.

This paper presents the design of an innovative scalable wind turbine emulator. The system’s hardware and software components are described in detail including test results, demonstrating the research potential of the proposed design. The uniqueness of the proposed solution lies in its scalability, achieved despite the use of physical devices with fixed parameters. This scalability allows for the flexible shaping of the system’s structure and parameters, allowing it to emulate both individual wind turbines with a capacity ranging from kilowatts to megawatts as well as aggregated models of entire wind farms. The emulator was developed using the hardware-in-the loop (HIL) concept and consists of a digital part including aerodynamic and mechanical models (wind, rotor, shaft models, disturbances, etc.) and control systems for mechanical and electrical devices within the wind turbine. The digital component has a modular structure, which allows for the replacement of any module of the native control system with the user’s custom designed one and testing its properties over a broad range of parameters. The test results presented in the article demonstrate a satisfactory level of accuracy of the developed emulator.