Performance management analytics - the next extension in managerial accounting

Transcription

1 Performance management analytics - the next extension in managerial accounting Riccardo Silvi a*, Klaus Moeller b, Marten Schlaefke c a Department of Management, University of Bologna, P.le della Vittoria, 15, Forlì Italy. Tel.: ; fax: riccardo.silvi@unibo.it b Faculty of Economic Science, Georg-August-University of Goettingen, Germany, Platz der Goettinger Sieben 3, Goettingen. Tel.: ; fax: Klaus.Moeller@wiwi.uni-goettingen.de c Faculty of Economic Science, Georg-August-University of Goettingen, Germany, Platz der Goettinger Sieben 3, Goettingen. Tel.: ; fax: Marten.Schlaefke@wiwi.uni-goettingen.de * Corresponding author 1 Electronic copy available at:

2 Abstract Hypercompetitive businesses are increasingly requiring the need for more rapid and sophisticated information and data analysis. This challenges management accounting s role in effectively supporting the management decision making process. The emerging area of business analytics can potentially extend the domain of management accounting to the comprehension of business dynamics and provide more solid inputs for managing its performance. This paper introduces performance management analytics (PMA) as the next extension in management accounting research and practice. Specifically, it defines PMA as the understanding of relevant business dynamics through the use of data and analytical methods. In addition, it suggests PMA as a relevant part of performance management systems and moreover as the missing link between the existence of highly sophisticated performance management systems and their effective implementation. Furthermore, it provides a framework that incorporates a set of key components (input, process, output, outcome) where performance is grounded in its context, captured, coupled, and controlled. This framework might be useful for research and practice in the area of performance management. The relevance of performance analytics might be a predestinated subject for further empirical research in the field of the advances in management accounting with specific regards to performance management systems. If relevance and effectiveness of performance management analytics will arise, education on management accounting would have to be redesigned and integrated with performance analytical topics and new skills and competences on IT and analytical methods might be required for management accountants. Keywords: performance management analytics, business analytics, performance measurement systems JEL classification: C67, M10, M40, L25 2 Electronic copy available at:

3 1 Introduction Since the mid eighties of the last century, management accounting has continuously evolved by expanding its scope and control objects. As a consequence of an increased and more aggressive competition, management accounting has indeed broadened its domain from conventional financial reporting and control task to an essential complement for the strategic decision making process (IMA 2008). Specifically, management accounting has extended its focus to the identification of the drivers of financial performance, both internal or external to business. New and revolutionary non financial metrics and approaches were added to the set of management accounting tools, with results that are still under investigation among theory and practice. Especially the field of (strategic) performance measurement has made not only with the Balanced Scorecard approach- considerable progress in the development of tools, techniques and scientific contributions. Many companies and markets are currently facing the consequences of operating in an hypercompetition environment where competitive advantages of organizations are no longer effective and new sources of competitive advantage are created as well as rapidly eroded (D Aveni 1994). New business opportunities, timing and know how as well as fast and effective execution require more sophisticated and analytical decision making tools at both operational and strategic level. The challenge for management accounting is to supplement this area with approaches that sometimes have to be framed by drawing competences and skills not properly related with the domain of management control. We refer to mathematics, statistics, econometrics or to IT technologies and data gathering and analysis tools. By adding these approaches to the domain of management accounting, new insights about business dynamics and their related performance can be explored and exploited and increased management effectiveness would be the result. In this paper we introduce the rise of performance management analytics as the next extension in management accounting and specifically, in performance management system research and practice. We propose that performance management analytics can be the missing link between the existence of highly sophisticated performance measurement systems (from a technical or instrumental view) and their effective implementation and use in order to increase performance. So far the relation between the distribution of such systems, their implications and the organizational success is inconclusive (Micheli/Manzoni 2010, p.2). Our aim is to contribute to the explanation of the reasons why, despite over twenty years of debating, these systems are still not largely implemented and why their effectiveness is controversial. Furthermore we propose a framework for performance management analytics that 3 Electronic copy available at:

4 merge the theoretically derived and practically elaborated challenges for an appropriate and useful design for research and practice. We first emphasize the need for performance management analytics by giving some field evidence (section 2), in the following section (section 3) we ground ourselves in performance management research. In section 4 we define analytical performance management and provide a possible framework for analytical performance management. Afterwards, we discuss the value added of performance analytics in the hypercompetition (section 5) and give some ideas for future research on performance management analytics (section 6). 2 The need for performance management analytics evidence from the field Organizations are in need of more advanced data analyses, scenario planning and even predictive capabilities because they are facing a rising complexity, higher uncertainties and volatilities in their businesses. At the same time, they have more data available than ever before (IBM; 2010). As a result, organizations start focusing to a greater extent on analytical approaches to deal with the data. The importance of business analytics is increasingly recognized in the academic literature, reflected in the raising number of publications per year displayed in figure 1. An overview of academic papers which were included is shown in figure trend Figure 1: Number of publications concerning business analytics via EBSCOhost ( ), total = 28 4

5 total Business Analytics (total) Decision Support Systems 3 2 Information Systems Management 2 1 Journal of Management Information Systems 1 Communications of AIS 1 1 IIMB Management Review Financial Executive 1 1 Journal of Internet Banking & Commerce 1 1 INFORMS Journal on Computing 1 1 Journal of Education for Business 1 1 Journal of Comparative Economics 1 1 CPA Journal 1 1 Intelligent Data Analysis 1 1 International Journal of Electronic Commerce 1 1 Journal of Revenue & Pricing Management 1 1 Journal of the Association for Information Systems 1 1 Strategic Finance Geospatial Solutions 1 1 Interfaces 1 1 International Journal of Productivity & Performance Management 1 1 Journal of Database Marketing & Customer Strategy Management 1 1 Journal of Corporate Accounting & Finance 1 1 International Tax Review 1 1 Communications of the ACM 1 1 Figure 2: Academic papers concerning business analytics via EBSCOhost ( ), total = 28 At this early stage, research on performance management analytics is exploratory by nature. Through interviews and surveys our research is mainly focused on the understanding of the research problem within management accounting and the real world potential of analytical approaches in performance management. Throughout several focused empirical surveys and a series of interviews with management executives we developed and sharpened subsequently the approach of analytical performance management. The following anecdotes from the interviews clarify the need of the users as well as the core requirements: 1. in our business (food industry) profitability is very low and it results on the way we effectively set prices Prices are based on the expected purchasing cost of raw material (olives). Its effective forecast is crucial for our bottom line. if I have a method to target this cost the bottom line would benefit as well as the entire organization (Business controller, July 2009). 2. we often submit offers to our customers when at the time of their submission the cost of electronics components we use for the product (digital devices) is higher than the price of the product we quote. It is not dumping, but these practice is related with the bet we do on future cost reductions of the components which regularly we got. Supporting these forecasts with detailed analysis might increase our margins (CFO, May 2010). 5

6 3. I would like to have someone from the controllership that can provide me an estimation of the relationship between marketing/advertising costs and related revenues. When they are effective, when they stop to generate adequate revenues (CEO of equipment company, November 2007). We might keep on reporting many other statements from interviews, but obviously management accounting should expand its focus and set of tools to the understanding of the drivers which make a decision profitable or not and business performance analytics might be the answer. The case of the olive oil business for instance extends to required analytics the forecast of dynamics that have an impact on costs (crop yield, quality, weather conditions, regulation, ), while a backward oriented report on costs and related variances, or supplier s cost analysis is less effective. At the same time, in the case of the electronic company having an evidence of price dynamics and drivers of their change might increase profit control. The third case raises the point of a deep understanding of the relationship between marketing investments and related returns. These examples might not unveil anything of absolutely new, but they clearly show that management accounting should consider the opportunity of providing analytical tools about performance factors, especially when data is potentially available and can be converted into business signals. New competencies and skills (mathematical, statistical, econometrics, IT) might be required to management accountants, but their value added in today s organizations might significantly increase. 3 Performance Management as a starting point 3.1 Approaches Performance management analytics can be seen as further refinement of performance management concepts. The following three approaches for a systematic development of performance management systems can be seen as core streams of research and application and thus are a starting point for performance management analytics (Berry et al., 2009, p. 6; Broadbent/Laughlin, 2009, p. 283): - Strategic performance management systems focus on the link between organizational objectives/strategy and performance measurement systems. A well known example is the balanced scorecard by Kaplan and Norton (Kaplan/Norton 1996, 2001, 2004, 2006, 2008). The main feature of these strategic performance management systems is that they link strategy to performance measures and to other systems including operations, human 6

7 resources, performance evaluations, information technologies, as well as to customer and supplier networks and the value chain (Chenhall, 2005). - The levers of control framework by Simons (1995) contributes more from an organizational perspective to the role, that a performance management system can play in an organization. Therefore Simons distinguishes four levers of control: interactive, diagnostic, belief, and boundary systems. A performance management system must not only be designed in an instrumental and functional way, its use must also be specified explicitly and communicated to the different usergroups. - Ferreira and Otley developed a very comprehensive performance management systems framework which outlines the possible key aspects of the performance management process and thus is providing a basis for a systematic discussion about potential elements of a performance management system (Ferreira/Otley, 2009, p. 266; Berry et al., 2009, p. 7). 3.2 Features A comprehensive performance management system (PMS) consists of multiple features (Ferreira/Otley, 2009, p. 266): - PMS should identify and communicate the vision and mission of an organization and show how they can be drawn to the attention of managers and employees. - PMS should identify the key success factors and illustrate how they can be brought to the attention of managers and employees. - PMS should illustrate the organizational structure and uncover how this structure effects their design and use. - PMS should illustrate the strategies and plans of an organization and show which processes and activities are required for their implementation. - PMS should identify and illustrate the key performance measures. - PMS should identify the appropriate performance targets for the key performance measures and show how they should be chosen. - PMS should identify the already existing processes of performance evaluation within an organization. - PMS should set the rewards for the target achievement. - PMS should illustrate the information flows that can support the activities of performance management. Concerning this wide range of possible features of a performance management system there is no prior assumption if all features have to be included in a holistic 7

8 performance management system and if the existence or absence of a particular feature is a good or a bad thing (Ferreira/Otley, 2009, p. 267). But it is obvious, that if these features are supposed to work properly, they do require a great amount of the right data. Therefore, many organizations tend to collect a lot of data concerning their performance in different business areas. This trend is due to the fact that companies have in mind, that they can only manage, what they are actually able to measure. But the truth is, by only measuring the performance they do not reach a competitive advantage. They need to find a way to understand the gathered performance data and transform it into information that effectively support the management and control of performance and thus make the features of performance management system executable (Mouritsen, 2004, p. 263). Referring to the five processes of business performance measurement by Franco-Santos et al the processes data capturing/analysis and the resulting information management in terms of information provision, interpretation and decision making are the ones that need to be examined in greater depth. Altogether, many companies have no shortage of performance data, but they are still lacking the connection between the data and subsequent management action. Therefore, we are focusing on analytical tools that may help to build such a connection and provide management accountants with additional information to improve the decision making process. 4 Analytical Performance Management 4.1 Definition In complex organizations we understand the task of performance management systems in the selective capturing, control and communication of tangible and/or intangible elements within a causality based coupling of inputs, processes, outputs and outcomes to improve the degree of organizational target achievement. Different methods can be used to identify and verify the mentioned causal couplings within the performance management. Recently, specific examples are visual maps and performance tree diagrams, (Kaplan/Norton 2004, Lynch /Cross 1991). When adopted, these methods can be qualitative or executed with a more sophisticated or scientific (analytical) level of design. While the ultimate intent of the qualitative approach is to capture organizational attention or to drive its behavior with no specific evidence of a measure of these cause and effect relationships, the analytical one seeks for the real magnitude of these interdependences. Moreover they potentially discover new or often hidden business dynamics both at a strategic or at an 8

9 executional level. The scientific search for these causal couplings and business dynamics is performance analytics. Specifically, performance analytics uses multiple sources, from drill down accounting data (i.e. revenue and cost breakdowns) to more sophisticated mathematical, statistical, or econometrics methods set for understanding and exploring the dynamics of performance factors. Therefore, the term performance management analytics describes the modelling or the understanding of relevant business dynamics through the use of data and analytical methods. The term analytical performance management describes the systematic use of these performance management analytics to identify, use and prove quantitative relationships among the context factors, inputs, processes, outputs and outcomes to support business value creation in the long run. Consequently, analytical performance management deals exclusively with the deployment of performance analytics and delivers crucial information to drive decisions and actions within the performance management (Davenport/Harris, 2007, p. 7). Analytical performance managements ultimate role is to effectively support the understanding, exploring and exploiting of business dynamics and opportunities. In our view, it is a part of a (strategic) performance management system that allows an organization to identify potential strategies and/or their effective execution. 4.2 Framework Nevertheless, there is still no approach that provides guidelines for the systematic coupling of all the elements of an analytical performance management in a comprehensive and application oriented framework. Such an approach may help managers to decide which kind of analytics they should use when they want to test and map causality based couplings of context factors, inputs, processes, outputs and outcomes. Such an approach could be the starting point for a standardized way of using analytics and therefore for the improvement of performance management and the application of performance management systems. With the multilayer performance management approach in figure 3 we give an idea how such an approach might look like. It consists of four layers with different tasks: - Layer A: Context The layer contains the internal and external context factors that influence the organization. It also considers the factors which occur by the business model. - Layer B: Capture The layer contains the capturing of performance drivers within the categories inputs, processes, output and outcomes. The capturing can be done with the 9

10 help of already implemented performance measurement systems or other data sources. Characteristics of the indicators (like e.g. being tangible or intangible) can be part of the capturing. - Layer C: Couple The layer contains the coupling of performance drivers and thus shows their proposed connections. The coupling can be done by proposing cause-andeffect relationships between the different indicators. When proposing causeand-effect relationships, time lags need to be considered as well. Business analytics can be used to verify the proposed causal relationships. - Layer D: Control With the knowledge of cause-and-effect relationships the crucial levers of control can be identified. Management actions and the design of management control systems can be deduced from the information about these causal relations. By using feedback loops the coupling of the performance drivers has to be revised regulary and an organizational wide continous learning is stimulated. For the purpose of planning aspects feedforward loops can be used as well. Furthermore, scenarios can be tested within the layer with whatif analysis. Layer A: Context Layer B: Capture Layer B: Capture Input Processing System Output Outcome A1.1 A2.1 A2.2 A3.1 A4.1 A4.4 A1.2 A2.3 A3.2 A4.2 A4.5 Layer C: Couple A1.2 A3.3 A4.3 A4.6 B1 B2 B3 B4 B4 B6 B7 B8 B13 B14 Layer C: Couple Layer D: Control Layer D: Control Figure 3: The multilayer performance management analytics framework 10

11 4.3 Areas and Applications A next step would be to develop guidelines based on the proposed multilayer performance management analytics framework that recommend the use of specific analytical approaches and instruments. In general, analytics can be used in every functional area of management like R&D, human resource, marketing etc. Hence, business analytics could be e.g. categorized as external analytics, supplier analytics, internal analytics and customer analytics as shown in figure 4. external analytics (competition, economics, technology ) supplier analytics internal analytics customer analytics Figure 4: Categories of business analytics Especially the following approaches can be supported by analytical applications: demand forecasting, price setting, customer value calculation and prediction, marketing effectiveness evaluation, brand and reputation effectuation etc. Such a list of tasks can only be an excerpt of all possible areas in which performance analytics can be of reasonable use but it already illustrates the wide range of aspects that performance analytics can be used for. Concrete examples of tasks for performance analytics were given in the anecdotes in section 2. The developed analytical performance management framework can be used to position these and to highlight, where the dominant value added of the analytical approach would be (see figure 5). 11

12 Layer A: Context Layer B: Capture Input Processing System Output Outcome Case A : forecastingof raw material prices A2.1 A2.2 A3.1 Case 1+2: A4.1 A4.4 effective pricing A1.2 A2.3 A3.2 A4.2 A4.5 A1.2 A3.3 A4.3 A4.6 B1 B2 Case 3: Case 3: effects of marketing/ cost effectiveness advertising costs B3 B4 B4 B6 B7 B8 B13 B14 Layer C: Couple Layer D: Control Figure 5: Application areas of business analytics (case examples) In order to an effective use of these performance analytics, there are some prior requirements that need to be fulfilled. Effective performance analytics underlie many aspects such as data availability, IT-infrastructure and related competencies, like e.g. business data analysis skills. The potential of an analytical management system is already in place in those organization that already have an advanced IT infrastructure such as: - Enterprise Resource Planing System (ERP) and Extended-ERP, - Data Warehouse, - Data Mining Systems or - Customer Relationship Management. Analytical approaches that support the performance management are everytime based on information systems. So performance management analytics evolve in the merge between different areas of expertise, especially management accounting and IT, combined with analytical methods (see figure 6). Furthermore different maturity levels can be divided (see figure 6): 1) Applications that focus on the identification and use of cause and effect relations for performance optimization on a logical level (management accounting applications). 2) Techniques that combine the logical reasoning with more complex and sophisticated mathematical, statistical or 12

13 econometric models (analytical methods). The interface between the IT-based applications, management accounting applications and the analytical methods is where effective performance management analytics are located. effective performance management analytics IT-based applications Data mining Textual analysis Natural language processing Classification and regression trees Neural network (strategic) management accounting applications Cost break down, cost driver analysis Activity based costing, process modeling, value stream mapping Life cycle costing, total cost of ownership Target costing Balanced scorecard analytical methods (econometric, mathematical, statistical) Regression analysis Data envelopment analysis Structural equation models Vector autoregressive models Markov chain-based models Bayesian models/networks Figure 6: Applications of analytical performance management Altogether the use of data, facts, and analyzing tools can be a powerful support for decision making and it can be assumed that the decisions made with their support are better than those that are made without data, facts and performance analytics. Their obvious advantages and their increasing importance within performance management make performance analytics a predestinated subject for further empirical research. 13

14 5 Performance analytics in the hypercompetition The rise of the relevance of performance analytics is undoubtedly due to the fast growing emerging of the hypercompetition effect (D Aveni 1994), where increased competition on cost and quality, timing, know how, strongholds and deep pockets between companies and markets rapidly create and erode competitive advantages. Performance analytics have the potential to provide quick evidence of competitive dynamics. They signal opportunities and threats, changes in markets and competitors behavior, internal process, customers and supply chain drivers of profitability. They can help to identify the key success factors by uncovering causal relationship and therefore visualize what truly drives financial performance. As part of performance management systems performance analytics create a basis for their effective application by delivering crucial information by capturing, combining and analyzing multiple sources of data (operational, financial, internal, external, qualitative and quantitative, ). Because of this assisting function as an information generator performance analytics are essential for a more fact-based decision making within performance management systems and thus can be a tool that creates a competitive advantage within the hypercompetition (Davenport/Harris/Morisson, 2010, p. 8). Within the hypercompetition managers are facing major challenges trying to stay competitive within their business. Paying attention to performance analytics may be one reasonable option for them to improve their performance management and consequently ensure or create a competitive advantage. Moreover, performance analytics can have multiple advantages for managers. Performance analytics can (Davenport/Harris/Morison, 2010, p. 3) help to manage and steer business in turbulent times. They support managers with tools to understand the dynamics of their business including how economic and marketplace shifts influence business performance. show what is really working. Rigorous analytical testing can establish whether your interventions are causing desired changes in your business, or whether it is simply the result of random statistical fluctuations. leverage previous investments in IT and information to get a closer insight, faster executions, and a higher business value in many business processes. cut costs and improve efficiency. Optimization techniques can minimize asset requirements, and predictive models can anticipate market shifts and enable companies to move quickly to slash costs and eliminate waste. 14

15 help managing risks. Greater regulatory oversight will require more precise metrics and risk management models. anticipate changes in market conditions. You can detect patterns in the vast amount of customer and market data coming your way. create a basis for improving decisions over time. If you are using clear logic and explicit supporting data to make a decision, anybody can examine the decision process more easily and try to improve it. While it is difficult to not agree with these advantages, the design and the use of an analytical performance management system require a thorough comprehension of the business model of the company, of its performance factors, its key success processes, information, data sources and the definition/formulation of the algorithms which convert data, transactions, events into management alerts and actions. 6 Outlook In the hypercompetition age performance analytics can be the missing link between already gathered data and the understanding of what the data really implies and means. Performance analytics is the understanding of relevant business dynamics through the use of data and analytical methods that can be of essential assistance for the features of performance management systems. In order to create the basis for their effective application they should be designed and used within a specific or explicit business model and related with the information needs of key success business processes. In addition, they have to be implemented at the executional level to empower management effectiveness and efficiency. Extending the domain of management accounting to business performance analytics might require new competencies and skills about data analysis but might improve management effectiveness. Their obvious advantages and their increasing importance within performance management make performance analytics a predestinated subject for further empirical research in the field of the advances in management accounting with specific regards to performance management systems. Specifically future research should be focus on the relevance of analytical performance management and its effective application context. On an instrumental level also the potential combinations and couplings among analytical methods and tools should be revealed. If relevance and effectiveness of performance management analytics are increasing, there will be consequences for teaching and training. Especially education on management accounting would have to be redesigned and integrated with performance analytical 15

16 topics. Furthermore new skills and competences on IT and analytical methods might be required for management accountants. 16

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