30 research outputs found
Data driven decision support framework for industrial smart services design and operations
No full textIntroduction / purpose: Smart services in an industrial context have the purpose of creating mutual value for the diverse actors of a business ecosystem (Baines & Lightfoot, 2013; Rapaccini & Adrodegari, 2022). To enable sustained value creation in dynamically changing contexts, managers must make decisions at different levels about allocating or reconfiguring service-oriented resources (Meierhofer et al., 2021). On an operational level, event-driven decisions, such as specific maintenance actions on equipment, are required (Schweiger et al., 2022). On a strategic level, there are decisions about resource reconfigurations, e.g., for adjusting the structure or capabilities of the service resources to a changing context. The decisions to allocate or reconfigure resources may have significant implications on operational costs or customer performance, thus customer satisfaction and loyalty (Schweiger & Meierhofer, 2023). Therefore, correcting decisions after their implementation generates high costs. With the data available in digitally enabled service systems, i.e., smart service systems, the decision makers can be supported by data-based models to a large extent and thus take better informed decisions, which reduces the risks for operational costs and customer performance. However, utilizing data-driven decisions represents a high hurdle to overcome, in particular for SMEs (Kugler, 2020). The purpose of this paper is therefore to develop a decision support framework that enables SMEs to overcome the hurdles for utilizing data-driven decision making in the design and operations of industrial smart services.
Theoretical background: The management decision process creates value through the following steps: (a) elaborating possible decision options, (b) assessing these options qualitatively or quantitatively, and (c) choosing the option with the best assessment. Qualitative management judgment can supplement quantitative assessment(Holsapple, 2008). Service-Dominant Logic (S-D L) can be used to conceptualize management decision support as a service (Vargo & Lusch, 2008). Management is supported by smart services in completing these decision jobs. With data-driven decision support services, quantifying the value provided can be traced back to the question of the value of applying data for deciding on service configurations (Meierhofer et al., 2022).
Research methodology: A quantitative online study was conducted based on a previous study on assessing the value of data and a series of interviews (Meierhofer et al., 2022). To support the need for decision support in smart service context, the results were used to develop a decision support procedure to help companies make better-informed decisions in the right context. The new procedure is being applied in specific cases to validate it.
Findings (actual / expected): SMEs recognize the importance of data for informing their decisions, but lack methodologies for quantitative decision support. To support this decision procedure and keep the costs of decision making manageable for SMEs, we developed a multi-step decision support framework. It starts with a qualitative analysis of the decision problem, followed by quantitative steps. In the first quantitative step, a rough calculation of the mutual value creation for different decision scenarios is derived based on system models of the service processes. On the strategic level, for example, this comprises different service configurations and their contribution to mutual value creation in the ecosystem. If this rough quantification does not provide sufficient actionable insights or detailed operational decision parameters need to be found, one moves to the second quantitative step of the procedure. Here, a more refined simulation model comprising the specific service processes is developed, allowing for the comparison of different service scenarios in detail. In a final step, one selects the optimum decisions. This step is semi-quantitative, as it combines the quantified outcomes of the models with human experience taking into account factors that cannot reflect technical data (Rowley, 2007).
Theoretical and practical contributions: The new decision support framework for smart services integrates service value creation concepts with quantitative modeling of business processes into a procedural framework. It thus advances the methodology for designing and operating smart service systems. Moreover, the framework has been developed and validated with industrial partners, making it designed to be applicable by practitioners in their business environments
Data driven decision support framework for industrial smart services design and operations
No full textIntroduction / purpose: Smart services in an industrial context have the purpose of creating mutual value for the diverse actors of a business ecosystem (Baines & Lightfoot, 2013; Rapaccini & Adrodegari, 2022). To enable sustained value creation in dynamically changing contexts, managers must make decisions at different levels about allocating or reconfiguring service-oriented resources (Meierhofer et al., 2021). On an operational level, event-driven decisions, such as specific maintenance actions on equipment, are required (Schweiger et al., 2022). On a strategic level, there are decisions about resource reconfigurations, e.g., for adjusting the structure or capabilities of the service resources to a changing context. The decisions to allocate or reconfigure resources may have significant implications on operational costs or customer performance, thus customer satisfaction and loyalty (Schweiger & Meierhofer, 2023). Therefore, correcting decisions after their implementation generates high costs. With the data available in digitally enabled service systems, i.e., smart service systems, the decision makers can be supported by data-based models to a large extent and thus take better informed decisions, which reduces the risks for operational costs and customer performance. However, utilizing data-driven decisions represents a high hurdle to overcome, in particular for SMEs (Kugler, 2020). The purpose of this paper is therefore to develop a decision support framework that enables SMEs to overcome the hurdles for utilizing data-driven decision making in the design and operations of industrial smart services.
Theoretical background: The management decision process creates value through the following steps: (a) elaborating possible decision options, (b) assessing these options qualitatively or quantitatively, and (c) choosing the option with the best assessment. Qualitative management judgment can supplement quantitative assessment(Holsapple, 2008). Service-Dominant Logic (S-D L) can be used to conceptualize management decision support as a service (Vargo & Lusch, 2008). Management is supported by smart services in completing these decision jobs. With data-driven decision support services, quantifying the value provided can be traced back to the question of the value of applying data for deciding on service configurations (Meierhofer et al., 2022).
Research methodology: A quantitative online study was conducted based on a previous study on assessing the value of data and a series of interviews (Meierhofer et al., 2022). To support the need for decision support in smart service context, the results were used to develop a decision support procedure to help companies make better-informed decisions in the right context. The new procedure is being applied in specific cases to validate it.
Findings (actual / expected): SMEs recognize the importance of data for informing their decisions, but lack methodologies for quantitative decision support. To support this decision procedure and keep the costs of decision making manageable for SMEs, we developed a multi-step decision support framework. It starts with a qualitative analysis of the decision problem, followed by quantitative steps. In the first quantitative step, a rough calculation of the mutual value creation for different decision scenarios is derived based on system models of the service processes. On the strategic level, for example, this comprises different service configurations and their contribution to mutual value creation in the ecosystem. If this rough quantification does not provide sufficient actionable insights or detailed operational decision parameters need to be found, one moves to the second quantitative step of the procedure. Here, a more refined simulation model comprising the specific service processes is developed, allowing for the comparison of different service scenarios in detail. In a final step, one selects the optimum decisions. This step is semi-quantitative, as it combines the quantified outcomes of the models with human experience taking into account factors that cannot reflect technical data (Rowley, 2007).
Theoretical and practical contributions: The new decision support framework for smart services integrates service value creation concepts with quantitative modeling of business processes into a procedural framework. It thus advances the methodology for designing and operating smart service systems. Moreover, the framework has been developed and validated with industrial partners, making it designed to be applicable by practitioners in their business environments
On the value of data : multi-objective maximization of value creation in data-driven industrial services
No full textThe paper was awarded the best paper awardData-driven value creation is a key topic in industrial services. However, designing such services in an optimal way represents a multidimensional and complex task. In this paper, we present a design methodology based on a simultaneous maxi-mization of value creation for both the provider and the customer, allowing the identification of optimal service configurations. We apply this methodology to a use case of a manufacturer delivering services for its machines in the context of a pay-per-use business model. The approach is based on modeling the value creation separately for both provider and customer, as a function of data-driven services which may be offered in different phases of the lifecycle. The model allows finding Pareto-optimal service configurations which provide value creation optimized simultaneously for both the provider and the customer. These optimal configurations are not easy to find with simpler methods because of non-linear effects in value creation along the lifecycle
Development approach for value-creating service process twins based on service design methods
No full textAdopting the digital twin concept in the industry has revealed its potential for value-creation in production and operations. At the same time, the idea of a digital twin is also being discussed in many other business areas. One of these is after-sales service, where the digital twin is attractive from two aspects. On the one hand, as a source of knowledge to better understand the customers’ problems. On the other hand, digital twins may help manage service operations themselves. However, building such a service process twin with limited resources and capabilities is not trivial. Moreover, managing service operations transparently can be complex and demanding. Therefore, it is essential to focus on the solutions that generate the most value for customers and providers. Based on literature research and previous projects with similar problem settings, this paper presents a method to build such service process twins. Thereby, service blueprints and agent-based simulation play a significant role. The presented systematic approach has been tested and continuously improved in two projects with companies in different industries. However, current research showed room for improvement concerning the detail level of service blueprints and their interrelationship with the simulation models
Private investment and macroeconomic adjustment : an overview
No full textThis paper reviews current investment theories, recent models linking macroeconomic policies and private investment, and the effect of uncertainty and credibility on irreversible investment decisions. Empirical studies on the subject are also reviewed, and the general implications of this literature for the design of growth-oriented adjustment programs are discussed.Economic Theory&Research,Environmental Economics&Policies,International Terrorism&Counterterrorism,Financial Intermediation,Banks&Banking Reform
From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system
No full textThe importance of waste management, including collection, separation, recovery, and recycling, increases with the growing amount of waste. Technological innovations such as smart connected products, the Internet of Things, and digital twins are driving the development of smart management systems. Investments in necessary product-service systems are justified by cost savings and improved service quality, especially in affluent societies like Switzerland. However, there is a trade-off between cost savings and service quality that raises the question of optimal balance. Using a Swiss municipality as an example, this paper models the trade-off between cost savings and service quality using waste bin sensor modules. Simulation results demonstrate the impact of cost savings on service quality reduction and that substantial cost savings are possible without a service quality compromise. We also introduce a digital process twin as a decision support system that is able to leverage a growing database. These results contribute to research, firstly through the field study with 98 waste bins equipped with fill level sensor modules, secondly through the model-based analysis of the trade-off between cost savings and service quality, and thirdly by conceptualizing a digital twin-based decision support system. The results further contribute to practice, firstly by providing benchmarks for implementing similar systems in other municipalities without having to create their own simulations, secondly by presenting an innovative key performance indicator to measure service quality, and thirdly with a model that can be used for simulations to determine the individual optimum between costs and service quality
Improving service value creation for manufacturing SMEs by overcoming data sharing hurdles in ecosystems
No full textPurpose: Although there is an apparent potential in using data for advanced services in manufacturing environments, SMEs are reluctant to share data with their ecosystem partners, which prevents them from leveraging this potential. Therefore, the purpose of this paper is to analyse the reasons behind these resistances. The argumentation paves the way for elaborating countermeasures that are adequate for the specific situation and the typical capabilities of SMEs.
Design/Methodology/Approach: The analysis is based on literature research and in-depth interviews with management representatives of 15 companies in manufacturing service ecosystems. Half of these are manufacturers and the other half technology or service providers for manufacturers. They are SMEs or partly larger companies operating in structures that are typical for SMEs.
Findings: Data sharing hurdles are investigated in the five dimensions, 1. quantifying the value of data, 2. willingness to share data and trust, 3. organizational culture and mindset, 4. legal aspects, and 5. security and privacy. The ability to quantify the value of data is a necessary but not sufficient precondition for data sharing, which must be enabled by adequate measures in the other four dimensions.
Originality/Value: The findings of this empirical study and the solution approach provide an SME-specific framework to analyze hurdles that must be overcome for sharing data in an ecosystem.
Manufacturing SMEs can apply the framework to overcome the hurdles by specific insights and solution approaches. Furthermore, the analysis illustrates the future research direction of the project towards a comprehensive solution approach for data sharing in a manufacturing ecosystem
GALERIA GEOGRAFICA DE CHILE: Don Andrés Bello López (1781-1865), la Cosmografía o Descripción del Universo conforme a los últimos descubrimientos y otros aportes a la Geografía
No full textThe obiective of this study is to show the work done by Andres Bello related with geography , as cientific journalist , translator, original author and Rector of the University of Chile.El presente estudio tiene por objeto dar a conocer la labor realizada por Andrés Bello en relación con la geografia, tanto como periodista científico, traductor, autor de obra original y Rector de la Universidad de Chile
