| Title | Developing Smart Services By Internet Of Things In Manufacturing Business |
|---|---|
| ID_Doc | 76099 |
| Authors | Cedeño, JMV; Papinniemi, J; Hannola, L; Donoghue, I |
| Title | Developing Smart Services By Internet Of Things In Manufacturing Business |
| Year | 2018 |
| Published | Logforum, 14, 1 |
| Abstract | Background: The aim of this study is to introduce a framework, which describes a customer-oriented approach for developing data-driven services by the Internet of Things (IoT), also known as "Smart Services" in manufacturing business. Methods: A literature review presents a background for IoT, data and digitalization of services, as well as the role of simulation technologies for the development process of Smart Services. For the empirical study, an agricultural tractor manufacturer case company was analyzed utilizing Customer Needs Assessment tools to determine the new service requirements. Results: The agricultural industry is also adopting the IoT related technologies to deliver services which are able to track crop environments in order to optimize crop yields and develop sustainable outcomes. The results obtained from the Need Interpretation Table and the Trace Matrix show that farmers need Smart Services to be able to connect their tractors and implements to the Internet for a better understanding of agricultural operations. They also demand a personalized everyday working plan based on data analyzed on previous work shifts. From the case company side, it is important to collect and analyze usage data, such as temperature, humidity and pressure of tractor environment, as well as data related to determine root causes and failure rates. Conclusion: The scientific contribution demonstrates the relationship between Product Lifecycle Management and IoT, and how it can leverage Smart Services. The practical contribution was based on the empirical study results by delivering smart service propositions in a tractor company, such as operation and remote failure tracking, showing customer demands for machine productivity, time saving and sustainable resource optimization. |
| https://doi.org/10.17270/j.log.2018.268 |
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