Project

Researching a fundamental methodology, KPIs and guidelines for integration of innovative ICT services in the care and welfare sector

Duration
01 January 2013 → 31 December 2016
Funding
Regional and community funding: IWT/VLAIO
Research disciplines
  • Engineering and technology
    • Computer hardware
    • Computer theory
    • Scientific computing
    • Other computer engineering, information technology and mathematical engineering
Keywords
ICT services smart ict servitization
 
Project description

Our environment is becoming ‘smarter’ Many objects, products and services both for personal and non-personal use are being taken to a next level due to the integration of ICT-functionalities. This allows these products and services to collect information of the context they are used in, and to communicate, adapt and act on this information. These ICT-enabled smart services have been contributing to the migration of many product-centered industries and businesses towards a service-centered structure. This trend, also known as ‘ervitization’ is driven by new capabilities and evolutions in the fields of Internet of Things (IoT) and cloud-services. Both IoT- and cloud enabled services open up a lot off new possibilities ranging from better customer insights, increased added value, increased cost-effectiveness and new types of revenue streams. Several application domains such as smart health and care (e.g. fall prevention and remote patient monitoring services), smart homes (e.g. Nest, a self-learning smart thermostat that allows energy provider to control your heating system), Industry 4.0 (e.g. machine monitoring services for preventive maintenance), smart utilities (e.g. smart electricity meters), smart cities and smart mobility (e.g.smart container monitoring) are growing at fast pace due to the potential benefits smart services hold. But, integrating these smart services comes with many new challenges and uncertainties for the involved actors, the users and the society as a whole. For instance new types of collaborations with other business actors need to be formulated, there are new and untested revenue models, and new investments have to be made. Also, how will the operational processes be affected and what about the effect of regulations or lack thereof? These uncertainties lead sometimes to a reticent attitude, or can even result in barriers for adopting the smart service. In the care sector for instance, research and statements of representatives of associations of physicians indicate that a lack of financial structures fuels reluctance, and therefore adoption of smart care services has been low. Also in other application domains such as smart homes and smart mobility there can rise concerns about data privacy and ownership, which can result in a lowered adoption of the new service. To gain insights in the overall impact of smart services and in order to create or formulate a viable and economic offer, they need to be evaluated very well. To do so, in this dissertation, we propose a methodology to evaluate the multidimensional impact of smart services. First, we focus on the impact a smart service has on the way business actors must collaborate, co-operate or compete with each other. This starts by analyzing the value network, which comprises the identification of involved actors, their tasks or roles within the complete value offer and the value exchanges between these actors (e.g. monetary vs. specific services). Value network analysis provides insights in how added value is created and allows identification of potential threats or gaps for a sustainable value network. A sustainable value network is a constellation in which all the involved actors receive value in such a way they benefit individually and therefore will further contribute in the value network. If this is not the case, the value network will become unsustainable and non-viable in the current setting. In order to know if the involved actors will or will not benefit from integrating the new smart service, the total impact on their business model needs to be identified and evaluated. Smart services can not only affect the individual business strategy and the business processes, but also lead to additional investments and potential revenue streams. We start with identifying and describing the added value and impact on the business model, which can be an increased market share, attracting a new type of customer, a service extension or a completely new offer for their customers. Next, current operational processes are broken down to identify how smart services will impact current process steps and task. Simulation methodologies such as discrete event simulation are used to model the effects the smart service has on predefined key performance indicators (KPIs) of the current operational processes. Quantifying the impact of these KPIs, as well as quantifying qualitative impacts such as increased peace of mind, increased mobility and less anxiety, allows economic analyses of the smart service. To do so, also the required investments or capital expenditures and operational expenditures need to be modelled. Scenario and techno-economic analyses then can guide us to the most optimal deployment strategies and feasible technical alternatives. At last, when the impact for each actor involved is described and quantified, we can evaluate the overall viability of the smart service, its value network and the individual business models. Due to their innovative and disruptive characteristics, smart services often face many barriers for large scale deployment. These barriers can be identified via PEST-analyses (political, economic, social and technological). A sustainable smart service not only is a result of the positive impacts on some of the involved actors’business models, nor on the viability of the complete value network but also of the lack of broader PEST-barriers. Taking into account all potential barriers and potential gaps in the business models of individual actors, guidelines and viable go-to-market strategies for the smart service can be formulated. The main research contributions of this dissertation are the formulation and validation of the proposed methodology and the description of the simulation and techno-economic models used via case research. First, the value network analysis for the integration of an eCare platform, which aim is to facilitate the communication and collaboration between patients and both the formal and informal care providers, whilst monitoring changes in daily patterns and lifestyle of the care receiver, shows us that there are currently many barriers for large scale deployment (e.g. technological barriers for elderly, lack of standards, privacy concerns, lack of financial structures). The analysis indicates that these barriers are too large to tackle at once. Therefore a migration path is presented to release the features and capabilities of the eCare platform in several steps in order to achieve complete patient-centric home care delivery. When analyzing the business models of the involved care providers, it has become clear that home care organizations could benefit from such an eCare platform because it simplifies administrative tasks. The direct costs for both the care scheduling and billing processes could be reduced by 38% by implementing such a smart eCare platform. In addition, a significant amount of time of the care personnel could be freed up for providing more qualitative care. Next, to evaluate the impact of an ontology-based nurse call system on the operational processes of the care department, the care staff and patients, a discrete event simulation model is presented. Such an ontology-based nurse call system takes into account a set of context-variables such as trust relationships, type of call (e.g. alert, care request, request for hotel services) and current agenda of the nurse staff when assigning a call to a specific staff member. Up to eight different implementation scenarios of an ontology-based nurse call system have been compared with traditional nurse call systems. The performance of the system is measured via different key performance indicators (KPI) such as workload balance, maximum waiting time before answering a call, number of redirected calls, and distance walked per shift. Comparing the KPIs of both traditional nurse call systems and an ontology-based nurse call system shows that the latter can result in increased operational performance in specific scenarios. This DES modelling approach therefore proves to be useful for managers to determine the potential impact of smart services on the operational processes. IoT is a major enabler for smart services. But introducing IoT-functionalities such as context monitoring and controlling actuators (e.g. motor, speaker, electro magnet) has an impact on the cost structures of the business models. The choice of the network technology influences not only the hardware, middleware and firmware cost, but also affects operational expenditures such as the costs for telecommunications and battery replacements. Both types of costs have a significant impact on the cost structure of a smart service provider, certainly in case of large scale deployments. To guide IoT-developers or service providers in choosing an appropriate IoT-connectivity technology for their service, a two-step methodology has been proposed. The first step narrows down a wide set of different network technologies (e.g. LoraWAN, Sigfox, BLE, Satellite based communications, GSM and LTE) based on mismatches between the technical characteristics of the networks and the required functionalities of the service (e.g. data payload size, maximum transmission frequency, range). In a second step, the total costs of the remaining IoT-connectivity alternatives are compared. This includes network deployment, network maintenance, device hardware costs, battery replacement, and tele-communication fees. This techno-economic approach allows smart service providers to choose the most economic and technically feasible network technology. At last, we present a multi-actor evaluating methodology that identifies and compares the added values and impact of various eCare services for the involved actors. This not only includes the quantification of the impact on the revenue and cost structures, but also of qualitative impacts such as increase in peace of mind and overall quality of life (QoL). Combining these benefits with the identified PEST-barriers for the involved actors provides insights in potential gaps in the business models which could lead to unsustainable value networks. In addition, the methodology also presents high level guidelines based on the type and magnitude of these barriers in order to be able to formulate a viable smart service offer for the involved actors. These guidelines or suggestions for improvement include for example: the service subscription cost should be lowered by a certain amount, the qualitative added value of the service should be increased or the upfront investment is too high.