| 1. |
- Hietala, H., et al.
(författare)
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Benefits Realisation in Post-Implementation Development of ERP Systems: A Case Study
- 2021
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Ingår i: CENTERIS 2020 - International Conference on ENTERprise Information Systems / ProjMAN 2020 - International Conference on Project MANagement / HCist 2020 - International Conference on Health and Social Care Information Systems and Technologies 2020, CENTERIS/ProjMAN/HCist 2020. - : Elsevier. ; , s. 419-426
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Konferensbidrag (refereegranskat)abstract
- Enterprise Resource Planning (ERP) systems influence organisational performance and business strategies significantly. However, identifying, managing, and realising expected benefits of ERP is challenging. The existing ERP research has largely focused on the implementation phase leaving post-implementation development understudied. Yet, post-implementation development ensures continuing benefits. This paper documents a single-case study on benefits realisation in post-implementation ERP development. The results imply that benefits realisation practices can improve job satisfaction while lack of it can lead to unused solutions. Outdated and evolved business processes, complicated further by workarounds, hinder on-going ERP development. Consequently, benefits realisation is hindered as well. Moreover, when subsidiaries are unable to identify new benefits on their own and while the benefits may even vary from one subsidiary to another, the complexity of benefits realisation increases. Altogether, the article highlights four empirical findings on issues influencing post-implementation benefits realisation and four issues on post implementation ERP development in general.
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| 2. |
- Hietala, Heidi, et al.
(författare)
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Modes of Collaboration in Digital Transformation of Municipal Wastewater Management
- 2021
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Ingår i: ECIS 2021 PROCEEDINGS. - : Association for Information Systems.
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Konferensbidrag (refereegranskat)abstract
- Digital transformation introduces new opportunities for public services, such as wastewater management. The opportunities include better interoperability and data availability, leading to such benefits as predictive maintenance and efficient allocation of resources. To reach the envisioned benefits, inter-organisational collaboration is essential. However, municipal water utilities have varying objectives, expectations, and challenges related to collaboration. This paper reports a field study on the digitalisation opportunities of municipal water services in Finland. We report the observed collaboration forms, rationale, expected benefits, and recognised challenges (i.e., the modes of inter-organizational collaboration) for deploying digitalised operations. This study provides insight into inter-organisational collaboration on the digital transformation of municipal water management. On a theoretical level, the study supports and complements the previously theorised collaboration modes of autonomous development, standardisation, and central service organization. The results also describe why and how limited company mode emerges in the sector, corresponding to the previously described consortium mode.
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| 3. |
- Peltonen, Ella, et al.
(författare)
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Towards Real-time Learning for Edge-Cloud Continuum with Vehicular Computing
- 2021
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Ingår i: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Sensor-driven IoT systems are well-known for their capacity to accelerate massive amounts of data in a comparatively short period of time. To have any use, the information delivery and decision making based on the data require efficient learning models together with dynamically deployed computing and network resources. The current cloud and high-performance computing infrastructures, as well as modern edge computing systems especially in the 5G and beyond networks, can be addressed to resolve these challenges. However, there are several application areas especially in vehicular and urban computing, where just harnessing more computational power does not solve computational and real-time requirements of the modern sensing systems that operate in mobile and context-dependent environments. For now, the mathematical challenges of distributed computing and real-time learning algorithms have not been profoundly addressed in the context of the IoT and real-world sensing applications. Data-driven systems also require giving full attention to information delivery, data management, data cleaning, and sensor fusion technologies that need to be equally distributed and real-time competent as the learning algorithms themselves. New software-defined computing and networking approaches and architectures are required to orchestrate the numerous connected resources dynamically, controllably, and securely along with the evolving needs. The key challenge here is to uniform collaboration between different aspects of the system, from data processing and delivery to the algorithms and learning models, not forgetting the computational capacity and networking capabilities, all this in real-time with real-world applications.
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