AN EA-BASED APPROACH TO VALUATE ENTERPRISE TRANSFORMATION: THE CASE OF IS INVESTMENTS ENABLING ON DEMAND INTEGRATION OF SERVICE PROVIDERS

Determining the value contribution of IS investments is a crucial task to support conscious decisions, e.g. about the scope or for or against the implementation of these investments. IS investments transform an enterprise not only in its IS related architecture, but often enable enhancements within the business related architecture. Valuating IS investments from an integral point of view therefore means to measure the value contribution to all affected artifacts of an enterprise. Enterprise architecture (EA) used as a coordinative framework to valuate enterprise transformation may help to support this goal. We propose a valuation approach for IS investments based on EA offering two advantages: As through EA all artifacts of and their relationships within an enterprise are known, the impact of IS investments on all architectural layers can be identified and attributed to the IS investments as an integral value. Furthermore EA provides (detailed) models of all artifacts changed. These models can be used to support the valuation of the IS investments? impact on all affected artifacts. To demonstrate how this valuation approach can be tailored for valuating a concrete IS investment, we apply it to the exemplary case of valuating an IS investment enabling the on demand integration of service providers. Therefore we model the enabled enhancements of this IS investment on the business and business process architecture, relating on the basic optimization problem of capacity planning within a certain business process. A case study of the payment transaction process of a banking transactions provider finally shows the applicability of the valuation approach

The Error Of Fixed Strategies In IT Innovation Investment Decisions

Allocating an IT Innovation budget to technologies in different maturity stages (mature vs. fashionable IT innovations) is a demanding task for companies. Due to the dynamic innovation cycles with new emerging technologies, many IT innovation investment decisions follow a bandwagon behavior or fixed investment strategies. Instead of optimizing the IT innovation budget’s allocation to mature or fashionable IT innovations and following a mindful investment strategy, fixed strategies with naïve diversification are the rule in practice. To contribute to the decision making process regarding the IT innovation budget’s allocation, we aim on the optimized allocation to mature and fashionable IT innovations via a dynamic optimization model incorporating the idiosyncrasies of IT innovations and a company’s innovator profile. Though determining the optimum in practice seems to be virtually impossible, we argue that deviating above or below the theoretical optimum leads to a substantial difference regarding the IT innovation budget’s value contribution. For that we examine the valuation error resulting from under- or overinvesting in mature and fashionable IT innovations due to deviating from the theoretical optimum. By providing our ex ante dynamic optimization model and analysis we contribute to the decision making process regarding the engagement in new emerging IT innovations

Modeling IT Availability Risks in Smart Factories

In the course of the ongoing digitalization of production, production environments have become increasingly intertwined with information and communication technology. As a consequence, physical production processes depend more and more on the availability of information networks. Threats such as attacks and errors can compromise the components of information networks. Due to the numerous interconnections, these threats can cause cascading failures and even cause entire smart factories to fail due to propagation effects. The resulting complex dependencies between physical production processes and information network components in smart factories complicate the detection and analysis of threats. Based on generalized stochastic Petri nets, the paper presents an approach that enables the modeling, simulation, and analysis of threats in information networks in the area of connected production environments. Different worst-case threat scenarios regarding their impact on the operational capability of a close-to-reality information network are investigated to demonstrate the feasibility and usability of the approach. Furthermore, expert interviews with an academic Petri net expert and two global leading companies from the automation and packaging industry complement the evaluation from a practical perspective. The results indicate that the developed artifact offers a promising approach to better analyze and understand availability risks, cascading failures, and propagation effects in information networks in connected production environments

DETERMINING OPTIMAL STRATEGIES FOR INVESTMENTS IN AN EMERGING IT INNOVATION

To generate competitive advantages through investments in emerging IT innovations, an economically well-founded investment strategy is of decisive importance, since timing and extent of investment amounts considerably determine the associated risk and return profile. Due to the uncertainty about emerging IT innovations, an early market entry time is associated with high risk, but offer high returns. A later market entry may carry lower risk but only offers lower returns. To take advantage of both investment strategies while reducing their disadvantages, a mix of both investment strategies can be advantageous. Companies often choose strict early or later investment strategies since an adequate assessment of possible combiniation opportunities and risks is not carried out in advance and company- and innovation-specific factors are neglected. Thus, we develop a quantitative optimization model enabling the determination of an optimal investment strategy and budget allocation to the two different investment strategies in the sense of maximizing the investment´s overall NPV supplementing previous studies by considering company- and IT innovation-specific factors. We show that strict investment strategies are often disadvantageous, that the amount of the investment budget influences the innovation´s expected NPV and that the company\u27s innovativeness has a strong influence on the innovation budget allocation

On the Ex Ante Valuation of IT Service Investments - A Decision Theoretical Perspective

The paradigm of service orientation and its incarnation in the form of service-oriented architecture (SOA) and information technology (IT) services play a crucial role in enabling companies to achieve considerable competitive advantages. However, to be able to leverage the opportunities of SOA and IT services, companies need to gain a thorough understanding of the business value of IT service investments. Nevertheless, research on IT services has focused mainly on technical questions so far; the economic perspective largely has been neglected. Therefore, the authors aim to contribute to the ex ante valuation of IT service investments from a decision theoretical point of view. Using decision theory as a theoretical base, the main aim is to identify and discuss specific challenges regarding the financial ex ante valuation of IT service investments, which arise from the inherent flexibility of IT services and the various interdependencies within a company’s IT service portfolio. The authors thereby emphasize that the application of common methods from financial theory for valuating IT service investments has to be treated with caution, as these methods are often tied to rather restrictive assumptions based on the specifics of capital markets. By analyzing different clusters of IT service investment decision problems using decision theory, the authors identify and discuss pitfalls that might occur when applying financial valuation methods to capture the flexibility and interdependencies of IT service investments. The decision theoretical considerations are intended to help build a solid basis for future multi-criteria valuation approaches, of which an essential component is a theoretically well-founded financial valuation

HOW AGILE IS YOUR IT DEPARTMENT? – DEVELOPMENT AND APPLICATION OF AN FRAMEWORK-INDEPENDENT AGILE SCALING MATURITY MODEL

Many IT departments seek to capitalize on the benefits of agile development by scaling agile practices. To manage the complex scaling, established approaches and frameworks promise guidance. However, although existing works envision a clear target state, they lack relevant capabilities along the scaling process, especially for vertical agile scaling. Managers need these capabilities to assess their company’s status quo and develop a clear scaling roadmap. Thus, within this work, we use the Design Science Research paradigm to build and evaluate a framework-independent agile scaling maturity model that provides management with a tool for ex-ante identification and evaluation of agile scaling capabilities in five maturity stages. To evaluate our model, we applied it at KUKA IT, the IT department of an international provider of automation solutions. As a result, this work provides insights into the application and outlines how IT departments can operationalize and utilize our model to guide agile scaling

ECONOMIC PERSPECTIVE ON ALGORITHM SELECTION FOR PREDICTIVE MAINTENANCE

The increasing availability of data and computing capacity drives optimization potential. In the industrial context, predictive maintenance is particularly promising and various algorithms are available for implementation. For the evaluation and selection of predictive maintenance algorithms, hitherto, statistical measures such as absolute and relative prediction errors are considered. However, algorithm selection from a purely statistical perspective may not necessarily lead to the optimal economic outcome as the two types of prediction errors (i.e., alpha error ignoring system failures versus beta error falsely indicating system failures) are negatively correlated, thus, cannot be jointly optimized and are associated with different costs. Therefore, we compare the prediction performance of three types of algorithms from an economic perspective, namely Artificial Neural Networks, Support Vector Machines, and Hotelling T² Control Charts. We show that the translation of statistical measures into a single cost-based objective function allows optimizing the individual algorithm parametrization as well as the un-ambiguous comparison among algorithms. In a real-life scenario of an industrial full-service provider we derive cost advantages of more than 17% compared to an algorithm selection based on purely statistical measures. This work contributes to the theoretical and practical knowledge on predictive maintenance algorithms and supports predictive maintenance investment decisions

Langfristige versus periodische IT-Investitionsbewertung im Rahmen einer wertorientierten Unternehmensführung

IT-Investitionen machen häufig einen sehr großen Anteil an denInvestitionsausgaben einer Unternehmung aus und gelten darüberhinaus als besonders riskant. Bei der Bewertung von ITInvestitionensollte deshalb deren Beitrag zur langfristigen undnachhaltigen Steigerung des Unternehmenswerts unter integriertenErtrags- und Risikoaspekten berücksichtigt werden. Entgegendiesem Ziel der langfristigen Unternehmenswertsteigerung stehtin der Unternehmenspraxis jedoch häufig eine kurzfristige Orientierungan periodischen Ergebnissen im Vordergrund. In dervorliegenden Arbeit wird anhand eines Optimierungsmodells einelangfristige und eine periodische Steuerung von IT-Investitionenverglichen. Es wird gezeigt, dass eine rein periodische ITInvestitionssteuerungaufgrund der Vernachlässigung intertemporalerAbhängigkeiten bei der Bewertung risikobehafteter ITInvestitionenzu langfristig suboptimalen Entscheidungen führtund dem Ziel einer langfristigen Steigerung des Unternehmenswertsnur unzureichend gerecht wird. Dieser Bewertungsfehler derperiodischen Steuerung wird in der vorliegenden Arbeit als Kostender periodischen Steuerung quantifiziert. Anhand eines praxisnahenFallbeispiels wird der Einfluss zentraler Parameter aufdie Kosten der periodischen Steuerung veranschaulicht

How Sustainable is Machine Learning in Energy Applications? – The Sustainable Machine Learning Balance Sheet

Information Systems play a central role in the energy sector for achieving climate targets. With increasing digitization and data availability in the energy sector, data-driven machine learning (ML) approaches emerged, showing high potential. So far, research has focused on optimizing ML approaches’ prediction performance. However, this is a one-sided perspective. ML approaches require large computation times and capacities leading to high energy consumption. With the goal of sustainable energy systems, research on ML approaches should be extended to include the application’s energy consumption. ML solutions must be designed in such a way that the resulting savings in energy (and emissions) are greater than the energy consumption caused using the ML solution. To address this need, we develop the Sustainable Machine Learning Balance Sheet as a framework allowing to holistically evaluate and develop sustainable ML solutions which we validated in a case study and through expert interviews