IoT Enabled Process Innovation: Exploring Sensor-Based Digital Service Design Through an Information Requirements Framework

The ongoing pervasive digitisation of physical artefacts and environments, often collectively referred to as the Internet of Things (IoT), signifies a new paradigm in data processing and communication. It is not a new technology per se, Wortmann and Flüchter (2015) describes IoT as “a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies”. IoT has evolved into an ecosystem of possible objects or things to connect to the internet where basically anything that has an on and off switch can be connected e.g. sensors. Combined with an expected battery life of several decades for many sensors allows for data collection around the clock. Sensors like this are making headway around the world with the emergence of smart cities, i.e. cities that are connected and efficient collecting data to manage its resources and serve its citizens in the best way possible (Sundmaeker et al. 2010). The global market for IoT is growing tremendously, resulting in significant market opportunities and an expected turnover at around 8.9 trillion dollars combined with an estimated 26–100 billion connected devices by the year 2020 (Statista 2017). Currently, there are significant uncertainties when companies engage in IoT; lack of standards, low understanding of technology and security issues are all sizeable problems hindering development (Forbes Insight 2017). Most companies feel uncertain which IoT-solution will collect the data they need, this results in a discrepancy in what companies need and what they get with regards to data and information (Forbes Insight 2017). In this paper, we argue that this uncertainty can be mitigated by synthesising two theoretical frameworks combining both analysis of information requirements and corresponding digital options with effects on processes by different IoT implementations.

We have been involved extensively in two IoT-projects and have seen issues regarding information requirements and process analysis arise throughout both. Both cases illustrate the need to understand the working environment and the technology at hand. While most studies thus far have focused either on the technical or business side of IoT (Forbes Insight 2017), we argue that there is a need to understand both aspects to generate successful outcomes. Since in most cases there is going to be humans interacting with the technology in their daily work life, we also argue that it is of great importance to understand the duality of technology and social activity. We have, therefore, focused on a micro-level perspective by investigating how IoT-systems affect work practices. This paper seeks to examine the effects of implementing sensor-based systems in organisations, i.e. how the implementation effects the process and value chain. This study will be limited to two separate case studies where sensor-based systems have been both designed and implemented in the organisations. Specifically, we explore the following research question:

With the emergence of ICT in the 20th century a lot of IT projects have had lacklustre results, one reason is the assumption that technological functionality solves organisational issues (Alter 2006). The argument against this is that an IT system should be viewed as a resource for the solution and not the solution itself (Alter 2006; Meyer et al. 2013). IoT is also argued to have a decentralising effect on the value chain in business as parts of it become connected. As the value chain becomes decentralised the decision-making rights are moved to the individual components in the chain, and the different parts become more independent, arguably creating a more efficient and streamlined process chain throughout (Haller et al. 2008). We would add to the current research by enacting these principles in real-world implementations. Doing so with the intention of showing real-world scenarios with the technology in place, we argue that the current body of research will benefit by the holistic perspective applied in this study.

For this paper we conducted a multiple case study consisting of two cases where sensor-based IoT-systems, through collection and visualisation of data, were designed and implemented to support different process chains. Case studies are a preferred strategy when research questions related to “how” and “why” are posed (Yin 2003), and multiple case-studies when the logic of the study is to “produce contrasting results but for predictable reasons” (Yin 2003, p. 47). We argue that this makes a case-study approach viable with regards to the framing and research question stated earlier. By evaluating this innovation process through multiple cases, we intend to generate general findings and propose practices which could be built upon in further research. We chose the cases based on an analysis of two different research sites. The classification for inclusion was that the case should present one or several concrete problems in a process chain where sensor-based technology could be a solution. These problems could either be a lack of ability in performing activities which could be enabled by the technology or addressing problems currently present in an organisation.

A process is “a structured, measured set of activities designed to produce a specified output for a particular customer or market” (Davenport 1993, p. 5) and can be classified into two different categories; operational processes and management processes (Mooney 1995). Operational processes are the set of activities an organisation performs to produce something that generates value and is referred to as an organisation’s primary activities. Management processes are related to streamlining and improving the efficiency of an organisation’s primary set of activities such as coordinating and handling different information. Process innovation in this context refers to the practice of analysing an organisation’s processes and redesigning them using innovative technology to improve performance and support the processes (Davenport 1993). In this case, that innovative technology is LoRa-sensors, which enables remote monitoring and control of different aspects of a process, and the IT-artefacts of the software designed to visualise or manipulate the data generated by the sensors. Each case process chain was broken down into sub-processes depending on what type of activities and the complexity of the tasks performed. We then mounted sensors at each research site to collect data and designed IT-artefacts with the purpose of solving specific problems related to the sub-process. The effectiveness of these systems was analysed in the context of what type of value and effects the data generated when innovating operational and management processes.

This section consists of the results of our research, each case will be presented with a description of the research site, problem background and information requirements of the process chains, with a subsequent description of practical implementation and effects from each case.

To provide actionable guidance for the use of IoT sensors to innovate processes through digital service design, we have illustrated the applicability of a synthesised framework facilitating opportunity recognition, design and analysis of effects. The analysis provides insight both to the general applicability of the framework across the innovation process, and the bearing of specific components of the framework for IoT sensors.

Although developed for different tasks, our application of the framework suggests that it is beneficial to apply the whole chain of analysis in the different subparts of the innovation process. While the business value of IT provides support for retrospective analysis of effects (Stenmark and Jadaan 2010; Visich et al. 2009), the desired outcomes in terms of informational, automational and transformational effects should guide the design of the digital service system. Thus, in accordance with Alters (2006) arguments regarding a holistic view in systems design, such ambitions need to be considered in the initial analysis phase. By establishing information requirements for a process chain or specific sub-processes during this phase, potential complexities related to generating the desired effects can be discovered, e.g. processes with a high level of equivocality. With a desirable effect-outcome and the information requirements necessary for generating this outcome established, we argue that this provides a more well-grounded basis for process innovation with sensors.

For sensor-based process innovation, the process information requirements in part determine the degree of automational effect that can be achieved. A process with the purpose of simply collecting or communicating one type of data may be automated in its entirety through implementing a sensor-based system. Automational effects of this kind are shown in the swimming pool case where the process chain of documenting pool water quality has the potential of being automated completely by the utilisation of sensor-based systems. This high degree of automational effects arises from alignment between information needed to complete the task and sensor capacity to produce data output. This outcome differs from a process with high equivocal information requirements, such as the process of evaluating the cleaning need where there are four sub-processes to complete the process chain. Each sub-process requires information with regards to its specific cleaning need, and the sum of the informational output from these sub-processes are then what constitutes the cleaning need of the classroom. The sensor used to support this process chain provides information of movement around the entrance to the classroom. This information of movement does not map precisely to any of the sub-process outputs of assessing a cleaning need. For example, to assess if a whiteboard needs cleaning there is a requirement of visual examination of the whiteboard and from this draw a conclusion regarding its need for cleaning. Information of movement is only a proxy variable that does not directly respond to use of the whiteboard. Similarly, to assess if the floor needs mopping, information is required on the amount of dust and dirt that is currently present on its surface. Again, the type of information provided by the sensor used does not immediately support an assessment of mopping need.

The sensor-produced data may be used to make assumptions of cleaning need based on the information of activity around the sensor, without showing a one to one relationship between the amount of movement and the equivocal cleaning need. For instance, while movement information does not show how dusty or dirty the floor is, it shows the cause of this effect: people have walked on the floor. Any value of activity data means that the floor has been walked upon. If the floor has been walked upon, it is reasonable to assume that some amount of dirt and dust have been transferred from the shoes to the surface of the floor. Thus, a value of activity data increases the need of mopping the floor.

This reasoning may be used on the other sub-processes of the cleaning case as well, though with a weaker conclusion. We have argued that movement information show a relationship with the degree of dirt on the floors. However, movement information is not directly correlated with the use of whiteboards. Recorded activity data means that there has been a person around the entrance of the classroom. It does, however, not capture the type of activity the person has engaged in, e.g. if they used the whiteboards. Similarly, the data does not show if the person(s) moving close to the classroom entrance also throw waste into the waste bin. The IoT sensor capabilities do not address these equivocal information requirements directly. Thus, conclusions regarding the cleaning need of these processes cannot be drawn solely from the information provided by the sensor. Movement information may, however, be used in conjunction with visual evaluation of the state of classrooms over time to show statistical probabilities of whiteboard cleaning need, waste bin level and table dirt. Cleaning staff collected this data during the testing phase of the cleaning case by grading the classrooms total cleaning need in conjunction with them cleaning it. This method of evaluation could be improved by splitting it into an evaluation of each sub-process, thus increasing its accuracy. There are degrees to this relationship between process information requirements and sensor data output. This relationship spans from misaligned, exemplified by assessing cleaning need of whiteboards with movement sensors, to aligned, as shown by addressing temperature requirements with a temperature sensor. Thus, we argue that IoT sensors could be implemented to processes with various degrees of success depending on the process equivocality information requirements (Table 4).

In this paper, we provide a synthesised framework for IoT sensor-based process innovation. The framework draws on extant theory on (1) the role of information requirements analysis in identification of IT-based process innovation opportunities and (2) effects on business value. The synthetisation of these theoretical devices enables a holistic analysis, from opportunity recognition to evaluation of achieved effects. We have explored the practical usability of the framework through an analysis of two different implementations of IoT systems and associated organisational effects. We found areas where the sensors have shown potential for process innovation and demonstrated the applicability of an information requirements perspective in an IoT context. Through this we have been able to identify areas where complexity becomes an issue for implementation of IoT systems. In particular, the results point to IoT sensors general capacity for responding to high connectivity and uncertainty requirements, and the need for aligning functionality with organisational needs to respond to high equivocality requirements. Thus, the functionality provided by IoT sensors does not reduce the importance of organisational ability for process analysis and identification of values to measure. We also identify the effects and relate them to levels of alignment between information requirements in the process and sensor capacity. Lastly, we have argued for a need to understand IoT through a micro-level perspective in organisational processes and proposed a set of practices for overcoming challenges encountered in the cases. As the field continues to grow we believe it is essential for organisations to further understand how to utilise IoT-technology when innovating their work processes.