Reducing the cost of government has become a major concern everywhere but is especially true in South Africa. There is recognition within government, at various levels, for the need for methodological innovation, but attempts at achieving this have been fragmented and disjointed.
Mr Johann Wolfswinkel, 07/06/2019
Reducing the cost of government has become a major concern everywhere but is especially true in South Africa. There is recognition within government, at various levels, for the need for methodological innovation, but attempts at achieving this have been fragmented and disjointed. The attempts have also suffered from a lack a theoretical framework that would underpin such efforts.
An example is the attempt to reduce redundancy in internal functionality by centralising replicated functions that can be found in a wide variety of government agencies (such as human resource management, accounting, logistics etc.). This was attempt can be found within the so-called “shared service” entities. These are credible efforts, but a far larger set of functions could be shared if the unit of analysis was made finer-grained. The sharing of finer-grained functionality comes with associated coordination costs, but with an underpinning conceptual framework, of the kind service science could offer, trade-offs could be analysed over the entire space of design alternatives. A key requirement for analysis of this kind is the ability to model (or document) government services (both internal and citizen-facing) in a sufficiently expressive, but standardised format.
Service science strives to addresses some of the major challenges of our times. It is a response to two inexorable historical trends.
Firstly, organisations are compelled to do more with less. Organisational productivity has, in general, trended upwards as human society has evolved, aided both by technological and methodological innovation. Secondly, driven by increasing competition, organisations are required to improve the quality of engagement with their stakeholders. Linked to these historical tendencies are some discontinuities – unique to our times. These involve the climate change challenge, as well as the enormous growth in (the global) population accompanied by an exponential growth in the demand for resources.
In essence, these phenomena impose on the world two significant imperatives: the efficiency imperative and the quality imperative. The former has been addressed to varying degrees by disciplines such as operations research, industrial engineering and computer science. The latter has been the focus of disciplines such as management and marketing. These imperatives, though being intimately inter-related, have been addressed in a fragmentary manner which inhibited progress. One cannot be addressed while ignoring the other.
Service science is an attempt to understand enterprise functionality in an integrated, holistic manner. It seeks to understand the way an enterprise conducts its business (or performs its functions), by using a service as the unit of analysis. Services might be delivered via the information communication technology infrastructure which are of interest to the service-oriented computing community. Alternatively, these services might be human-mediated; typically, the focus of interest of the services management and marketing community.
The development of a comprehensive body of knowledge to support the design and delivery of services has been lacking. Service science attempts to addresses that gap, by synthesising results from information science, computing, information systems, resource management and operations research (amongst several others). It includes in its ambit of inquiry questions such as the following:
- How do we model services?
- What components of enterprise functionality are best packaged as a service?
- How can we obtain a service-oriented view of the enterprise?
- What are best-of-breed methodologies to support the development of service designs?
- How do we improve service designs and hence service delivery?
- How do we manage the complete life- cycle of a service?
- How do we ensure that the services offered by an enterprise are aligned with its strategic objectives?
- How do we ensure that services comply with applicable laws and regulations?
- How do we optimise service delivery?
These are just a representative subset of a much larger repertoire of questions that should be addressed. Internationally, there is a growing recognition of the value that such thinking might deliver.
It is possible to develop service modelling approaches. Coupled with elements of enterprise modelling and enterprise architecture techniques, these could form the basis for whole-of-government service architectures. This would support the identification of intersecting functionalities and capabilities within disparate parts of government. It would also support the strategic alignment of these capabilities. Understanding and leveraging such insights in service design and subsequent execution poses real-world challenges. Such government service architectures would also provide a critically needed basis for change management.
Changes need to be made to several services. By developing a range of analytical tools it would be possible to identify the impact of the change across the whole of government. Ultimately, these techniques also provide the basis for the normative design of government service architectures, encompassing both internal capabilities and citizen-centric/driven services.
Any framework that supports design must also support the maintenance of designs in the face of change. A key trigger for design maintenance is the need for service improvement, which in turn relies on machinery for monitoring and assessing the quality of the functionality delivered. The service science engine includes approaches such as semantic mining of social networks to understand citizen responses to the quality of government services. The deployment of techniques for assessing quality of service can also add value in helping understand the effectiveness of government services and programs that indirectly impact citizens, and where citizen responses or sentiment does not form a viable basis for assessment.
Distinct from service design optimisation is service delivery optimisation. This involves answering questions about who delivers what service, when and how. These questions have traditionally been the preserve of disciplines such as operations research, industrial engineering and computing. Service science thinking adds value to these approaches by asking these questions, not in isolation, but in the context of the others discussed above.
Thus, service delivery optimisation must be addressed in the context of the design of government service architectures, as well as detailed citizen engagement models. Service delivery optimisation in the enterprise context also throws up new challenges that should be considered, such as the optimal service provisioning problem.
Consider the following example: given a specific service, its detailed design, expected service demand and some service-level guarantees, what is the optimal team size necessary to deliver this service without violating the service-level guarantees? The answer to this question relies on machinery that performs optimal task allocation – given a service request, what is optimal allocation of the tasks involved in processing this request to individuals or roles?
References and readings
- Qiu, R., Service Science: The Foundations of Service Engineering and Management, John Wiley & Sons, Jul 3, 2014
- Carroll, N. (2012). Service Science: An Empirical Study on the Socio-Technical Dynamics of Public Sector Service Network Innovation, PhD Thesis, University of Limerick
- Carroll, N., Whelan, E., and Richardson, I. (2012). Service Science – an Actor Network Theory Approach. International Journal of Actor-Network Theory and Technological Innovation (IJANTTI), Volume 4, Number 3, pp. 52–70.
- Carroll, N., Whelan, E. and Richardson, I. (2010). Applying Social Network Analysis to Discover Service Innovation within Agile Service Networks, Service Science, Volume 2, Issue 4, pp. 225–244.