Business Performance Analytics (BPA) entails the systematic use of data and analytical methods (mathematical, econometric, statistical) for performance measurement and management. Although potentially overcoming some traditional diagnostic issues related to Performance Management Systems (PMS), such as information overload, absence of cause-effect relationships, lack of a holistic view of the organization, research in the field is still in its infancy. A comprehensive model for operationalising analytics for diagnostic and interactive PMS is still lacking. Adopting an action research approach, this paper addresses this gap and develops a five-step framework applied to a company operating in the construction industry. The results show that in addition to encouraging dialogue, BPA can contribute to identifying critical performance variables, potential sources of risk and related interdependencies. A number of critical issues in implementing data-based approaches are also highlighted including data quality, organizational competences and cultural shifts.

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Purpose – For over 20 years, management control literature has indicated the importance of supporting the strategy development and implementation process with strategic performance measurement systems (SPMS) and integrating traditional financial indicators with a set of multidimensional forward-looking measures focusing on the long term and linked to cause-effect relationships. Nevertheless, knowledge on the specific SPMS models used in practice and their effectiveness in supporting the managerial decision-making process is still fragmented and ambiguous. The purpose of this paper is to first analyse the SPMS models used in practice, also considering the role of strategy and firm size as drivers of adoption, thereafter analysing the capability of SPMS models to provide managers with measures that are consistent with their strategic information needs. Design/methodology/approach – The research is based on a survey involving 88 Italian medium-large sized firms (or subsidiaries of multinational firms) operating on a global level. Findings – The cluster analysis identifies two very different SPMS models used in practice. The first is the Short-term Financial Model, and as its name indicates, is based on short-term, internally focused and unconnected financial indicators. The second is the Multidimensional Additive Model, which integrates financial and non-financial measures but without a fully developed fit with the strategy. The research primarily indicates unsatisfied information needs in both clusters, presenting a significant challenge to the further development of existing SPMS models and in defining new theoretical SPMS frameworks. Practical implications – The adoption of an incremental approach to SPMS, simply adding new operational and strategic non-financial measures without a real fit with the strategy does not increase the information effectiveness of the system. Originality/value – The paper analyses the characteristics and use of SPMS models in practice from an exploratory perspective, defining and applying a model to evaluate the information effectiveness of SPMS.

Bank branch efficiency measurements range from simple ratio and standard regression analyses to more complex frontier approaches, each with specific strengths and weaknesses. However, in isolation, the indexes that these approaches generate fail to capture the multidimensional nature of bank branch efficiency. This paper develops a three-step procedure that enables combining the strengths of the existing approaches. We begin by taking the widest number of efficiency indexes proposed in literature (step 1), reduce the redundant information through a collinearity analysis (step 2), and categorize bank branches into efficiency classes through a clustering procedure (step 3). We test our approach on 23 branches of an Italian regional bank. The results show that this three-step approach is able to provide a multidimensional view of efficiency based on indexes employing a wide range of theoretical and methodological approaches with different ways of conceiving (intermediation vs. production) and measuring (stock, vs. flow, or physical measures) bank outputs and inputs, different definitions of efficiency (technical and cost efficiency), and different measurement approaches (ratios, standard regression analysis and frontier functions). The resulting efficiency ranking is consistent with those that univariate indexes generate and provides a balanced evaluation of branch efficiency when such indexes produce contradictory indication

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Total cost of ownership (TCO) is a management accounting technique that evaluates the total cost of a business partnership using a time-consuming activity-based costing procedure. Studies have suggested that TCO-based data envelopment analysis (DEA) can effectively estimate the results of TCO with substantially less effort and time; however, its adoption in practice is limited due to certain shortcomings. First, managers struggle to understand and accept the uncommon weighting schemes of existing TCObased DEA models because traditional TCO analyses require a common set of weights. Second, both the traditional TCO approach and TCO-based DEA models are designed to handle precise data, whereas TCO analyses often involve imprecise data from conflicting data sources and estimations. To address the managerial and technical issues of handling weighting schemes and imprecise data, this paper proposes a novel TCO-based model: common set of weights imprecise DEA (CSW-IDEA). We validate the proposed methodology using real-life datasets from 175 suppliers that serve five key components to two multinational mechanical manufacturers. For both precise data and imprecise data, the proposed CSW-IDEA reliably approximates traditional TCO calculations significantly better than existing TCO-based DEA. The cost savings that can be theoretically generated by applying the CSW-IDEA approach are similar to the cost savings estimated by the traditional TCO approach.

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