An Approach for Portfolio Selection in Multi-Vendor IT Outsourcing

Companies increasingly extend their outsourcing strategies from single-sourcing to multisourcing combining best-of-breed vendors. This paper includes an analytical model to evaluate a company’s multisourcing strategy. The model can be applied for decision support to answer the questions, how many and which outsourcing vendors to integrate in the implementation of an IT project. We identify an optimal vendor portfolio considering monetary benefits and risk diversification as well as transaction costs arising from the integration and coordination of outsourcing vendors. Based upon a simulation, we find that it makes good economic sense to include a risk evaluation into the multisourcing decision process even if it is subject to misestimation. Therewith, companies are able to avoid unnecessary high risk and consequently a possible high damage. Furthermore, we find that it is better to be too cautious in risk assessment than to be too negligent

A Quantitative Model for Using Open Innovation in Mobile Service Development

The potential of mobile service innovations to create valuable economic impact makes their development desirable for companies. To develop and launch successful mobile services, the integration of customers in the idea generation process bears high potential. However, such Open Innovation activities usually demand for investments, whereas the precise relation between the money invested and the generated economic effect is still indistinct. The objective of this paper is to replace the black box between investments in Open Innovation and the thereby generated profits through formal-deductive analysis. For this purpose, we analyze the effect chain between Open Innovation and economic profit by adapting the model of Kano and putting special emphasis on the specifics of mobile services. Building on that, we develop a quantitative formal model to determine the optimal investment amount in Open Innovation activities for mobile services. The model’s utility is illustrated with an example based on real-world data

SIMULATING EXOGENOUS SHOCKS IN COMPLEX SUPPLY NETWORKS USING MODULAR STOCHASTIC PETRI NETS

Almost all major companies are embedded in complex, global supply networks, consisting of multiple nested supply chains, and building up a high level of complexity. Exogenous shocks on these networks (e.g. natural disasters) can directly and indirectly impact companies and even cause their entire supply network to fail. However, today it is extremely difficult for a company to predict the actual impact of an exogenous shock on its supply network. Hence, companies are not able to identify adequate counteractive measures. Therefore safeguarding measures are oftentimes insufficient or even counterproductive. This paper deals with modelling, analyzing and quantifying impacts of exogenous shocks on supply networks using Petri Nets. It provides means to simulate the vulnerability of different network constellations regarding exogenous influences. In order to evaluate the proposed method, we simulate different intensities of an exogenous shock delaying the delivery for an exemplary supply network. We thereby illustrate which results could be yielded from a real-world application. For our exemplary network we find that the marginal effect of a disruption declines with an increasing intensity of shock. Moreover, the impact of shocks can be mitigated by appropriate counteractive measures like in this example by an increased safety margin of stock

Energy Cooperatives as an Application of Microgrids: Multi-Criteria Investment Decision Support

The future of energy generation is expected to become increasingly decentralized. Today, many customers are already more than demand units, they also act as energy producers (prosumers) and thus participants in the energy market. The development of energy cooperatives with underlying microgrids in recent years undermines this observation. Information and communication technologies enable the management of energy cooperatives by incorporating data (smart meter, energy generation). We present a MAUT-based software tool to provide support for energy cooperatives when deciding about investments in new supply units. The findings of the literature analysis point out that energy cooperatives have economic, ecologic and social goals. Within our software tool we define measures for the fulfilment of the goals including available data. The utility for each goal is calculated and aggregated to an index. We test the developed software tool with real-world data. The results indicate that our artifact provides useful decision support

Demand response through automated air conditioning in commercial buildings - a data-driven approach

Building operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in regard to future electricity prices and demand. The decision is made based on an ex-ante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach

AN ECONOMIC ANALYSIS OF SERVICE-ORIENTED INFRASTRUCTURES FOR RISK/RETURN MANAGEMENT

Risk/return management has not only evolved as one of the key success factors for enterprises especially in the financial services industry, but is in the times of the financial crisis crucial for the survival of a company. It demands powerful and at the same time flexible computational resources making it an almost ideal application for service-oriented computing concepts. An essential characteristic of service-oriented infrastructures is that computational resources can be accessed on demand and paid per use. Taking the estimation of covariances for a portfolio of risky investment objects as an example, we propose quantification for the economic value of fast risk/return management calculations. Our model analyzes the influence factors on the optimal computing capacity dedicated to these calculations and reveals interesting insights in how far the optimal computing capacity depends on market parameters. Our main result is that more volatile markets require a lower computing capacity as the optimal computing capacity depends positively on changes of the market risk but negatively on the risk itself

Cross-Organizational Workflow Management Using Blockchain Technology - Towards Applicability, Auditability, and Automation

Bringing Blockchain technology and business process management together, we follow the Design Science Research approach and design, implement, and evaluate a Blockchain prototype for cross-organizational workflow management together with a German bank. For the use case of a documentary letter of credit we describe the status quo of the process, identify areas of improvement, implement a Blockchain solution, and compare both workflows. The prototype illustrates that the process, as of today paper-based and with high manual effort, can be significantly improved. Our research reveals that a tamper-proof process history for improved auditability, automation of manual process steps and the decentralized nature of the system can be major advantages of a Blockchain solution for cross-organizational workflow management. Further, our research provides insights how Blockchain technology can be used for business process management in general

Maximizing Smart Charging of EVs: The Impact of Privacy and Money on Data Sharing

Smart charging has the potential to shift peak load to times of lower demand, which better exploits renewable generation and enhances grid resilience. For increased effectiveness, smart charging requires access to data that consumers might be hesitant to share. To explore which data consumers would share and which factors influence this decision, we adopt the Barth and de Jong’s risk-benefit calculation framework to smart charging and conduct an online-survey (n = 479). We find that most respondents who would share charging details with a smart charging application, are ambivalent about location data and would never share calendar details. When presented with concrete monetary rewards, participants lose their initial reservations and would share all data for an amount dependent on the data’s sensitivity. Thus, our study contributes to research on the privacy paradox by highlighting the importance of calculations between perceived risks and benefits for the decision to share data

DON’T SLIP ON THE ICO – A TAXONOMY FOR A BLOCKCHAIN-ENABLED FORM OF CROWDFUNDING

Blockchain is rapidly evolving and there is an increasing interest in the technology in both practice and academia. Recently, a blockchain use case called Initial Coin Offering (ICO) draws a lot of attention. ICO is a novel form of crowdfunding that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although, more than 4.5 billion USD have been invested via ICOs, the phenomenon is poorly understood. Scientific research lacks a structured classification of ICOs to provide further insights into their characteristics. We bridge this gap by developing a taxonomy based on real-world ICO cases, related literature, and expert interviews. Further, we derive and discuss prevailing ICO archetypes. Our findings contribute to theory development in the field of ICOs by enriching the descriptive knowledge, identifying design options, deriving ICO archetypes, and laying the foundation for further research. Additionally, our research provides several benefits for practitioners. Our proposed taxonomy illustrates that there is no one-size-fits-all model of ICOs and might support the decision-making process of start-ups, investors and regulators. The proposed ICO archetypes indicate how common ICOs are designed and thus might serves as best practices. Finally, our analysis indicates that ICOs represent a valid alter-native to traditional crowdfunding approaches