Service productivity in the context of the service life cycle

Transcription

1 Service productivity in the context of the service life cycle Florian Kicherer 1, Peter Hottum 2, Sabine Janeschek 3, Bernd Bienzeisler 4 1 University of Stuttgart, 2 Karlsruhe Institute of Technology, 3 EBS Business School, 4 Fraunhofer Institute The productivity of services is a complex subject and goes well beyond the ratio of input to output. To understand service productivity within the different stages of the service life cycle an approach is being explored that examines 3 layers: individual, organizational and macro. The authors provide a research outline on how to measure, manage and optimize service productivity in dynamic systems. Implications for service providing organizations are presented. 1. Introduction Complex real world services are more and more created and consumed in dynamic systems of providers, customers, suppliers and partners. In such network like structures the creation of value is influenced by external factors (Vargo and Lusch 2006; Pinhanez 2009). Currently existing measurement approaches for process efficiency and performance are being used to determine the productivity of services, yet do not cover their dynamic peculiarity precisely enough (Grönroos 2004). Beyond the scope of measuring service productivity itself issues concerning the organizational structures that need to be established in order to design, manage, and optimize service productivity. In this context service productivity is of major interest for both industrial companies and research organization. A preceding industry survey from 2010 addressed industrial companies approach towards service productivity (cp. Fraunhofer IAO 2010). More than half of the surveyed companies claimed to have KPIs to measure and assess productivity of services. However, only 6 out of 28 different given answers concerning possible measures for productivity allow for a clear mapping towards service related productivity. So far there seems to be no clean cut distinction between performance indicators, measures for quality issues, resource deployment, and service productivity. The survey also states that only a quarter of the surveyed companies do not measure service productivity at all. However, the issue of service productivity is regarded as important, since it might give competitive advantage. Process optimization and staff qualification are seen to have the highest influence on service productivity by the participants of the survey. 1

2 2. Service Productivity reflected by the scope of the Service Lifecycle Looking at established literature productivity has been defined as the ratio of output and input factors, which are transformed by a service or a system (Wessels 2006; Coelli 2005; Varian 1999). With regard to this transformation of factors from a perspective where value is created through processes, the involvement of the participating partners in the measurement of service-productivity is necessary. Measuring productivity can be considered to be a manifold problem. Among other factors (cp. Djellal and Gallouj 2008) it is on the one hand difficult for companies to measure the value added, e. g. the contribution of actions of service entities to monetary aggregates such as a nation s GDP. On the other hand, it is very complicated to measure an increase in service productivity relative to the actions of an individual or a small group as tasks are rarely stable over time. Moreover from a service-oriented perspective, individuals are almost never isolated because they interact and belong to groups and therefore the actions of individuals are characterized by cooperation and value co-creation. With regard to concrete input and outputs factors which companies should concentrate on in order to increase their service productivity, the authors, following extensive research, realised that input factors have been largely neglected by academic research. However, concerning output factors, existing research indicates that companies should pay attention to a framework considering the multiplicity of services and especially the multiplicity of outputs. In this context, different types of output can be linked to different perspectives called»worlds«. These worlds are characterized by concepts or sets of outputs and attributes, which facilitate to evaluate those outputs. In the following the authors choose this concept and reflect the implications of these six types of worlds (cp. Djellal and Gallouj (2008)): The industrial and technical world in which output factors are characterized primarily in terms of volume (volume of claims, volume of contracts etc.), technical operations, and flows. The market and financial world in which the output factors are described mainly in terms of financial transactions and value. The relational or domestic world, which focuses on empathy and interpersonal relations as well as on the quality of these relations as a driver to estimate the output factor. The civic world, which is mainly concerned with justice, fairness, and an equal treatment of social relations. The world of innovation, which is characterized by inspiration and creativity and The world of reputation with its main focus on brand image. The six worlds clearly indicate that monetary measures for output do not provide a holistic representation of service productivity. In addition other output factors need to be considered as well. 2

3 With the shift of a bigger part of producing companies towards more service focused organizations, like Rolls Royce and IBM (cp. Baines et al., 2007; Jetter, Satzger, Neus, 2008), the idea of value co-creation becomes more and more important. To consider only the input and output factors of the established producer-centric understanding of productivity is not suitable anymore, because resources and capabilities of partners are influencing the common value co-creation (cp. Vargo and Lusch, 2004). In line with the principles of the concept of service dominant logic, where service is seen as the application of competences [...] through deeds, processes and performance for the benefit of another entity or the entity itself" (Vargo and Lusch, 2004), we select typical real world scenarios. In these constellations, the application of the value creation as well as the estimation of service productivity depends on all involved parties. These involved parties build a service system, where former producers and customers interact more and more as partners. Their interaction is based on partnership in the long run. The contribution of each partner to perform a service is measureable by the resources they integrate like information and technology, but also other people or departments. It depends on the scope how the borders of these service systems are arranged, which parties are involved as partners, how the objectives are met, and of course how the service productivity is measureable. In our research scenario we focus on different perspectives. In cases the interaction is happening mainly between two partners, the borders of the according service system as well as the interactions are clear. For a common measurement of the service productivity the relevant factors of both parties have to be considered. In the case that more than two parties are involved, as it is if one partner builds the bridge between two others, it gets even more complex to define objectives for the value co-creation and to evaluate the resources as well as the results which the parties put into and get out of the common value creation. In addition to the different worlds of production and scenarios described above the understanding of service productivity might be very specific in particular phases of the service lifecycle. Current literature offers a wide variety of different service lifecycle models. Widely recognized examples are: Service Lifecycle model according to Potts (1988), which emphasises a management perspective. Revenues are mapped over time to visualize profitability aspects of a certain service. The Customer Service Lifecycle which centres around the customer s point of view (cp. Ives and Willinger (1999)). A strong focus is set on e-commerce and service purchasing decisions. Service Lifecycle according to Katzan (2008) incorporates the management and customer perspective into one single model. Central to the model is the incorporation of a forward oriented scheme of matching a company s strategy to individual services to their operation and improvement. The improvement aspect also includes a feedback cycle to adapt to market requirements. 3

4 Within the scope of our research a Service Lifecycle Management model is deployed, that consists of the phases service engineering, service management, and service delivery (cp. Figure 1). It focuses on the service portfolio of a given organization. Figure 1: The Service Productivity Lifecycle as it is used in the ServUp project context. Service productivity is influenced by the dynamics of the different phases of the service lifecycle. However, which characteristics of a service lifecycle are needed to assess the productivity of a service? Within a given service portfolio there are usually services that have reached different phases of their lifecycle in terms of revenue, market sales, or customer demand. Does a service that is in a matured phase of its lifecycle have a different productivity than a newly introduced service? This depends on the perspective. From a customer s point of view it should not make a huge difference, as long as the service is well designed and delivers the promised utility. From a service provider s perspective the service s productivity might vary, depending on what is considered to be the exact input and output. If the service provider s perspective is being focussed, the goal of that organization should be to create a service portfolio that offers the highest productivity. To gather the information that is necessary to make such assessments, already existing services need to be monitored. This data shows whether or not the organization s goals are achieved in respect to productivity constraints. In the ideal case both internal productivity and productivity of the service consumer/partner should be maximized. 4

5 However, controlling via measurements in the service management phase is by no means the only data source to allow for measurement and evaluation of the productivity of services and/or a service portfolio. Data that is collected in the actual service delivery is very valuable to assess the productivity which impacts the service consumer/partner. The real life service interaction provides direct feedback from the service consumer. Furthermore the service provider has the chance to gather additional information on site and in an informal way, e.g. trough observation or personal contacts. This data, if deployed right, is used to stimulate new service development. In a service engineering process new services are designed to maximize productivity, both internal (the company s own productivity) and external (the customer s productivity). In order to achieve this, data from the service management and service delivery phase is being used. At the same time, new insights from the service engineering phase are channelled to already existing services, optimizing them or helping to decide whether or not to replace them. 3. Research outline for measuring, managing and optimizing service productivity in dynamic systems Our current research is embedded in a governmental funded collaborative research project focussing on the productivity of services. The scope of the project»servup«(bmbf reference number 01FL ) is on services that are product related and mainly focussed on manufacturing, machinery, and tooling industries. In a collaborative research project three scientific partners and three industrial companies work together. Within our research approach we are going to reflect our educed model with data from the project s three industry partners. These industry companies have a strong intrinsic motivation to assess, manage, and optimize their service productivity. The project s research partners will aggregate the results and transform them into models that will be made available to a broader public as well as the research community. A broad scope of the understanding of service productivity is applied. To understand service productivity within the different stages of the portfolio oriented service life cycle (service engineering, service management, service delivery) three main issues need to be considered: What are the benefits a service consumer and other involved parties gain from increased service productivity? This addresses aspects of the effectiveness of a service. From the perspective of the service provider these are issues of economic resource allocation, but also work load, and interface design. The customer s point of view is being incorporated e.g. via requirements analyses and/or integration into the design process. What are effects within the service providing entities? This addresses efficiency related aspects. What are the service provider s benefits? Are productivity levels as expected? A KPI based system will be designed to gain hard fact based information about the inputs and outputs. Service management will observe the service provider s own processes, performance, and resources and map these against defined output dimensions. 5

6 What are the effects that all individuals experience who are involved in the value exchange process? This addresses service work, thus flexibility issues. Service delivery is in most cases an interactive process. Within this interaction both service provider and service consumer/partner share information. This information is very vital to assess the service productivity and gives impulses for further service development. Will service providers be able to fulfil their service tasks and gather intelligence about further needs and expectations of the service partner? Service staff will need a strong understanding of the factors influencing service productivity. The research work elaborates what might be adequate indicators and how to measure service productivity by using these indicators. In most cases service productivity is measured in the service management phase. However as outlined above gained information from these controlling and monitoring activities will be made available to those responsible for service delivery and service engineering. Service delivery needs an awareness of preceding and following steps in the value creation. Service engineering transforms new found insights about effectiveness and efficiency into new services, maximizing internal and/or external productivity. To capture this, each one of the phases service engineering, service management and service delivery is assigned to one of the ServUp project s industry partners. One partner examines how service productivity can be considered while designing new services. A central aspect is the incorporation of impulses gained from newly available technology into the services and the usage of machinery data. Which elements of a service and especially which process steps influence service productivity? To implement a productivity oriented service engineering process the value creation of particular services needs to be carefully examined: Who benefits? Is a service supposed to increase the service provider s productivity or is it increasing the service customer s productivity? Another partner explores how service productivity can be measured and evaluated. The chosen solution approach will be centred on a KPI system. This KPI system has to be able to assess the service provider s own productivity within the processes and operations. The KPI system also needs to adequately quantify the customer s productivity respectively the gains in it through the service. Since a service management perspective is being chosen, all relevant indicators need to be monitorable/controllable, manageable, and to be in accordance with strategy goals. Aspects of service pricing and cost calculations will also be benefitting from the increased availability of productivity related data. The third partner approaches service productivity from a service delivery point of view. The focus is on the interaction between the involved players in the process, efficient information sharing, adapting to ad hoc changes, and necessary process interfaces. Individual skills of those involved in the service/value creation need to be carefully matched to the task. This holds true for own staff as well as the service customer s ability to perform the tasks that are assigned to him. Like in a lean production framework the industry partner will develop a concept of service value stream mapping Based on the service lifecycle model educed in chapter two and the results from the industry partners works, a research outline is set up to enable decision makers/takers to better understand their achieved service productivity. 6

7 4. Outlook, Limitations and Recommendations Looking at the status of the 2010 industry sample survey on service productivity, which indicates the relevance of the topic, as well as on the ServUp project consortium, we call for approaches that clearly support the measurement of productivity in service systems. Who benefits from a service that has been deliberately designed with productivity aspects in mind? Is the partner buying the service because his productivity gets increased How much would he pay for it? What are the influences on value creation? Is there shared productivity creation? How to design and implement KPI based productivity measurement systems to manage service productivity? Within the ServUp project these aspects will be addressed. Since the project is deliberately focussed on industrial and product-related services this limits the ability to generalize the project s results. A transformation of deducted insights and models needs to be considered and tested. For service providing companies the recommendations are obvious: Service productivity needs to be carefully assessed. The entire service portfolio needs to be taken into account. Available data from service management and delivery seems to be a valuable source for the productivity centred design of new services. Vice versa service engineering might provide valuable insights for the management of current service portfolios and the optimal delivery of services. Service productivity itself needs further research. What are the inputs? What are the outputs? What is an adequate level of aggregation considering particular goals and restraints? Industrial companies and research organizations alike try to find answers to these questions. 7

8 References Baines, T.; Lightfoot, H.; Evans, S.; Neely, A.; Greenough, R.; Peppard, J.; Roy, R. et al. (2007): State-of-the-art in product-service systems. Journal of Engineering Manufacture, 221 (10), pp Coelli, T.; Rao, D. S.; O'Donnell, C.; Battese, G. (2005): An Introduction to Efficiency and Productivity Analysis. Springer. Djellal, F.; Gallouj, F. (2008): Measuring and Improving Productivity in Services. Issues, Strategies and Challenges. Cheltenham: Edward Elgar Publishing. Fraunhofer IAO (2010): Stimmungsbild Produktivität von Dienstleistungen, Eine Kurzerhebung der strategischen Partnerschaft Produktivität von Dienstleistungen, 15 April 2011, Grönroos, C.; Ojasalo, K. (2004): Service productivity Towards a conceptualization of the transformation of inputs into economic results in services. Journal of Business Research, 57 (4), pp Ives, B.; Willinger, T. (1999): To Dell or be Delled: A Leading Edge View of Electronic Commerce. ISRC Notes, September Jetter, M.; Satzger, G.; Neus, A. (2008): Technological Innovation and Its Impact on Business Model, Organization and Corporate Culture IBM s Transformation into a Globally Integrated, Service-Oriented Enterprise. Business & Information Systems Engineering, 1 (1), pp Katzan, H. (2008): Service Science: Concepts, Technology, Management. A Guide to Service Science for Academicians and Practitioners. Bloomington: iuniverse. Kramer, J.; Kicherer, F.; Bienzeisler, B. (2011): The Service Life Cycle: Productivity in the Context of Service Specific Phases. In: Spath, D.; Illg, R.; Krause, T. (Eds.) (2011): Innovation in Product and Production. 21 st international conference on production research ICPR 21. Conference Proceedings. Pinhanez, C. (2009): Humans Inside as the Key Characteristic of Service Systems. Proceedings of QUIS 11, pp Potts, G. W. (1988): Exploit Your Product s Service Life Cycle. Harvard Business Review, 66 (5), Vargo, S.; Lusch, R. (2004): Evolving to a New Dominant Logic for Marketing. The Journal of Marketing, 68 (1), pp Vargo, S.; Lusch, R. (2006): The Service-Dominant Logic of Marketing. M.E. Sharpe. Varian, H. (1999): Intermediate Microeconomics. Norton. Wessels, W. (2006): Economics. Barron's Educational Series. 8

9 Authors Florian Kicherer University of Stuttgart Institute for Technology Management and Human Factors IAT Nobelstr. 12, D Stuttgart, Germany Peter Hottum Karlsruhe Institute of Technology (KIT) Karlsruhe Service Research Institute (KSRI) Englerstr. 11, D Karlsruhe, Germany Sabine Janeschek EBS Universität für Wirtschaft und Recht i. Gr. EBS Business School Chair of Services Marketing Rheingaustr. 1, D Oestrich-Winkel, Germany Bernd Bienzeisler Fraunhofer Institute for Industrial Engineering IAO Nobelstr. 12, D Stuttgart, Germany Key words 1. Productivity 2. Service Lifecycle 3. Measuring 4. Controlling 5. Service Work 6. Performance Sub-theme addressed by paper: Towards broader measurement of service productivity. 9