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The Balance Scorecard (BSc) constitutes one of the most important tools developed in recent years supporting the strategic planning and change management of large firms and organisations. The main advantages of BSc are that: a) provides a multi-dimensional measurement system for organizational success based on the four pillars (Financial, Customers...
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... With the aid of visual techniques the DM articulates the ranges of the relative importance between two commencing criteria or set of ex aequo criteria concluding to the identification of the ranges [zminr, zmaxr]. Figure 3 presents the way the limits of the zr index range are identified by the DM. Scroll bars are used to assist the visualization of the difference of the relative importance between two successive criteria or ex ... ...
Citations
... Thus, in order to describe the key components of these four aspects of a business, a BSC employs an array of measurements, ratios, indexes of significant accomplishments, and target goals [13]. As a result, the importance of the BSC is about determining the company strategy, breaking it down, and implementing it, all of which are critical phases for a startup looking to introduce new products [14]. ...
... Through their investigation into the Multicriteria Decision Aid WAP method's integration into the BSC, [13], arrived at the conclusion that this method's application is highly beneficial for a business due to its many advantages. This research employs both the theoretical framework of change management and mathematical models via linear programming. ...
Machine Learning (ML) can be proved as an important tool in planning better business strategies. For the purposes of the present study, the prospect for the development of an electronic platform by a technology firm providing financial services is explored. The purpose of this article is to demonstrate the ways in which a start-up can predict the success of an online platform prior to its market launch. The prediction is achieved by applying Artificial Intelligence (AI) on Key Performance Indicators (KPIs) derived from the customers' perspective, as shown in the Balanced Scorecard (BSC). The research methodology was quantitative and online questionnaires were used to collect empirical quantitative data related to bank loans. Subsequently, KPIs were created based on the collected data, to measure and assess the success of the platform. The effectiveness of the model was calculated up to 91.89%, and thus, it is estimated that the online platform will be of great success with 91.89% validity. In conclusion, prediction was found to be crucial for businesses to prevent a dire economic situation. Finally, the necessity for businesses to keep up with technological advances is highlighted.
... It is a tool that supports strategic planning and change management by diffusing the strategy and vision of the organization to each and every employee (Kaplan and Norton 1996). Additionally, the BSC facilitates change management as the goals of this process are linked to specific actions and timetables (Salmon et al. 2019). Finally, the implementation of the action plan is measured through the indices which have been assigned to each and every aforementioned perspective. ...
Artificial Intelligence (AI) has revolutionized the way organizations face decision-making issues. One of these crucial elements is the implementation of organizational changes. There has been a wide-spread adoption of AI techniques in the private sector, whereas in the public sector their use has been recently extended. One of the greatest challenges that European governments have to face is the implementation of a wide variety of European Union (EU) funding programs which have evolved in the context of the EU long-term budget. In the current study, the Balanced Scorecard (BSC) and Artificial Neural Networks (ANNs) are intertwined with forecasting the outcomes of a co-financed EU program by means of its impact on the non-financial measures of the government body that materialized it. The predictive accuracy of the present model advanced in this research study takes into account all the complexities of the business environment, within which the provided dataset is produced. The outcomes of the study showed that the measures taken to enhance customer satisfaction allows for further improvement. The utilization of the proposed model could facilitate the decision-making process and initiate changes to the administrational issues of the available funding programs.
... Identifying and analyzing the intricate relations among the four aforementioned perspectives is of great importance in the implementation of the Balanced Scorecard. An important finding in the relevant literature is that this is because the changes which take place affect each other in a non-linear form (Salmon et al. 2019). In each and every perspective, the Balanced Scorecard places emphasis not only on result-oriented criteria concerning what should be achieved, but also on criteria targeted as to how it should be achieved. ...
... These indicators are connected with specific targets, which have a predetermined timetable of materialization. The last vital step according to Salmon et al. (2019), concerning the achievement of the aforementioned targets, is that specific action plans will be selected, which also have predefined timetable and budget constraints. Falle et al. (2016) in their research presented a solid example of the development and implementation of the Balanced Scorecard in an SME, illustrating various supporting factors and challenges which affect this process. ...
The performance measurement of a great variety of enterprises is a highly complicated issue, especially taking into account that performance has a great many aspects and many variables which may, at times, be highly inconsistent with each other. The use of analytics and advanced machine learning promotes the decision-making process for each and every organizational structure. This paper combines the Balanced Scorecard and predictive analytics in order to assess the performance of a co-financed European Union program, which addressed 4071 Greek Small and Medium-sized Enterprises (SMEs) that requested funding. The application of predicative analytics tools and metrics in the available dataset of all addressed SMEs reveal the M5 Model Tree regressor to be an overall best prediction model for estimating the effect of the evaluation of companies' funding proposals on their financial results after the finalization of the co-financed program.
