Process Characteristics and Process Performance Indicators for Analysis of Process Standardization
(2018)
Industrial service companies deliver technically complex services (inspection, maintenance, repair, improvement, installation) for an enormous variety of technical assets in the chemical, steel, food and pharmaceutical industry. This variety of assets leads to a corresponding variety of service processes. To ensure competitiveness, the management of industrial service companies aims to increase the service process efficiency, especially through service process standardization. However, decision-makers struggle to make knowledge-based decisions on service process standardization because ex-ante the cost-benefit ratios of process standardization are unknown. The missing understanding of cost-benefit ratios of process standardization is caused by a missing understanding, which interdependencies exist between process characteristics and process performance indicators. Thus, the objective of this paper is to determine suitable characteristics and performance indicators to measure the way service provision processes are executed in the industrial service sector. The results represent the basis for executing an empirical questionnaire study focusing on the execution of service provision processes and identifying the cause-effect relations of process standardization.
Erfolgreiche Serviceinnovation im Zeitalter industrieller, datenbasierter Dienstleistungen unterscheidet sich deutlich von bisherigen Ansätzen der klassischen Dienstleistungsentwicklung. Diese Erkenntnis konnte aus einem breit angelegten Benchmarking in der deutschen Industrie gewonnen werden. Die Benchmarking-Studie identifizierte besonders erfolgreiche Unternehmen, deren Methoden und Ansätze zur Gestaltung innovativer Dienstleistungen in Form von Fallstudien im Detail untersucht wurden. Als Kernergebnis ergeben sich sechs Prinzipien, die erfolgreiche Serviceinnovation für datenbasierte Dienstleistungen auszeichnen.
Data-driven services play an important role in
innovative business models of successful manufacturing
companies: They hold great potential for the creation of unique
selling points and improve the differentiation of manufacturing
companies in highly competitive markets. However, the large
number of newly invented digital services that fail shortly after
launching implies that companies struggle with the invention and
implementation of data-driven service solutions, which ends in a
waste of resources. The following paper introduces guideline
principles for successful innovation processes for data-driven
services. The principles were identified during in-depth case
studies with manufacturing companies. They contribute to a
necessary paradigm change for manufacturing companies in
terms of data-driven services for machines. The six identified
principles emphasize new aspects regarding the new dimension of
data-driven solutions and improve the life cycle management of
products and services. They demonstrate how the rules of agile
development can lead to successful and more efficient service
innovations in the industrial sector.
Smart Service Engineering
(2018)
Global manufacturing companies currently face an increasingly turbulent economic environment known as the "VUCA-world" (volatility, uncertainty, complexity and ambiguity). After the transformation of many companies from product to solution providers in the last 15-20 years, the focus of many corporate change processes is on digital solutions such as data-driven services. In this context, service development is of particular relevance for industrial services. Companies develop digital strategies and try to maximize the added value for their customers, by offering, for example, smart services. They are based on smart products, which are connected to the internet, interact with their environment and gather environmental data. The collected data sets are combined with other easily accessible information and processed into so-called smart data. Based on this smart data, smart services are designed. They can be defined as individualized combinations of physical and digital services. They generate added value for providers and customers and offer context-related and demand-oriented value via digital platforms. The contribution of this paper to this research field of data-driven services is a service engineering approach for industrial smart services.
Since the 1990s, service engineering has established itself as a systematic process for the development of services. Currently existing service engineering processes are based on engineering science and business model innovation toolsets. However, the increasing digital components in service engineering reveal deficits in the direct application of the classical methods of service engineering to smart services. We suggest that the successful development and implementation of smart services requires a more agile service engineering process. Studies show that companies who develop services successfully (top-performer) act up to six times faster than those with less success (follower). They involve customers in the first running prototype of their digital service to increase customer centricity and focus their development activities on core functionalities of the service to reduce its development time and test it early with customers.
To strengthen the successful development pf data-driven services in future industrial service development projects, this paper contributes to a more agile service engineering approach. Smart service engineering combines elements of linear phase models and implements agile and customer-centric findings to decrease the overall development time by focussing on core functionalities that offer a high value for customers. The paper focuses on the service development steps and presents strategic scenarios for smart service engineering. It presents the interaction and interconnection of different elements of smart services based on a case study research. In addition to this, it illustrates the implications of a customer-centric engineering approach and possible strategic decisions based on the customer feedback. The paper focuses on the successful application of the smart service engineering approach and its impact in a German medium-size company in the textile machine industry.