---
_id: '45793'
abstract:
- lang: eng
text: The global megatrends of digitization and sustainability lead to new challenges
for the design and management of technical products in industrial companies. Product
management - as the bridge between market and company - has the task to absorb
and combine the manifold requirements and make the right product-related decisions.
In the process, product management is confronted with heterogeneous information,
rapidly changing portfolio components, as well as increasing product, and organizational
complexity. Combining and utilizing data from different sources, e.g., product
usage data and social media data leads to promising potentials to improve the
quality of product-related decisions. In this paper, we reinforce the need for
data-driven product management as an interdisciplinary field of action. The state
of data-driven product management in practice was analyzed by conducting workshops
with six manufacturing companies and hosting a focus group meeting with experts
from different industries. We investigate the expectations and derive requirements
leading us to open research questions, a vision for data-driven product management,
and a research agenda to shape future research efforts.
author:
- first_name: Khoren
full_name: Grigoryan, Khoren
last_name: Grigoryan
- first_name: Timm
full_name: Fichtler, Timm
id: '66731'
last_name: Fichtler
orcid: https://orcid.org/0000-0001-6034-4399
- first_name: Nick
full_name: Schreiner, Nick
last_name: Schreiner
- first_name: Martin
full_name: Rabe, Martin
last_name: Rabe
- first_name: Melina
full_name: Panzner, Melina
id: '72658'
last_name: Panzner
- first_name: Arno
full_name: Kühn, Arno
last_name: Kühn
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
- first_name: Christian
full_name: Koldewey, Christian
id: '43136'
last_name: Koldewey
orcid: https://orcid.org/0000-0001-7992-6399
citation:
ama: 'Grigoryan K, Fichtler T, Schreiner N, et al. Data-Driven Product Management:
A Practitioner-Driven Research Agenda. In: Procedia CIRP 33. ; 2023.'
apa: 'Grigoryan, K., Fichtler, T., Schreiner, N., Rabe, M., Panzner, M., Kühn, A.,
Dumitrescu, R., & Koldewey, C. (2023). Data-Driven Product Management: A Practitioner-Driven
Research Agenda. Procedia CIRP 33. 33rd CIRP Design Conference, Sydney.'
bibtex: '@inproceedings{Grigoryan_Fichtler_Schreiner_Rabe_Panzner_Kühn_Dumitrescu_Koldewey_2023,
title={Data-Driven Product Management: A Practitioner-Driven Research Agenda},
booktitle={Procedia CIRP 33}, author={Grigoryan, Khoren and Fichtler, Timm and
Schreiner, Nick and Rabe, Martin and Panzner, Melina and Kühn, Arno and Dumitrescu,
Roman and Koldewey, Christian}, year={2023} }'
chicago: 'Grigoryan, Khoren, Timm Fichtler, Nick Schreiner, Martin Rabe, Melina
Panzner, Arno Kühn, Roman Dumitrescu, and Christian Koldewey. “Data-Driven Product
Management: A Practitioner-Driven Research Agenda.” In Procedia CIRP 33,
2023.'
ieee: 'K. Grigoryan et al., “Data-Driven Product Management: A Practitioner-Driven
Research Agenda,” presented at the 33rd CIRP Design Conference, Sydney, 2023.'
mla: 'Grigoryan, Khoren, et al. “Data-Driven Product Management: A Practitioner-Driven
Research Agenda.” Procedia CIRP 33, 2023.'
short: 'K. Grigoryan, T. Fichtler, N. Schreiner, M. Rabe, M. Panzner, A. Kühn, R.
Dumitrescu, C. Koldewey, in: Procedia CIRP 33, 2023.'
conference:
location: Sydney
name: 33rd CIRP Design Conference
date_created: 2023-06-27T13:46:45Z
date_updated: 2023-06-27T13:57:42Z
department:
- _id: '563'
- _id: '241'
keyword:
- Product Management
- Data Analytics
- Data-Driven Design
- Product-related data
- Lifecycle Data
- Tool-support
language:
- iso: eng
publication: Procedia CIRP 33
status: public
title: 'Data-Driven Product Management: A Practitioner-Driven Research Agenda'
type: conference
user_id: '66731'
year: '2023'
...
---
_id: '29380'
abstract:
- lang: eng
text: Cyber-physical systems generate and collect huge amounts of usage data during
operation. Analyzing these data may enable manufacturing companies to identify
weaknesses and learn about the users of their products. Such insights are valuable
in the early phases of product development like product planning, as they facilitate
decision-making for product improvement. The analysis and exploitation of usage
data in product planning, however, is a new task for manufacturing companies.
To reduce mistakes and improve the results, companies should build upon a suitable
reference process model. Unfortunately, established models for analyzing data
cannot be easily applied for product planning. In this paper, we propose a reference
process model for usage data-driven product planning. It builds on three well-established
models for analyzing data and addresses the unique characteristics of usage data-driven
product planning. Finally, we customize the model for a manufacturing company
and demonstrate how it could be implemented in practice.
author:
- first_name: Maurice
full_name: Meyer, Maurice
id: '77201'
last_name: Meyer
orcid: 0000-0003-0606-7321
- first_name: Ingrid
full_name: Wiederkehr, Ingrid
id: '38169'
last_name: Wiederkehr
- first_name: Melina
full_name: Panzner, Melina
id: '72658'
last_name: Panzner
- first_name: Christian
full_name: Koldewey, Christian
id: '43136'
last_name: Koldewey
orcid: https://orcid.org/0000-0001-7992-6399
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Meyer M, Wiederkehr I, Panzner M, Koldewey C, Dumitrescu R. A Reference Process
Model for Usage Data-Driven Product Planning. In: Proceedings of the 55th Hawaii
International Conference on System Sciences. ; 2022:6105-6114.'
apa: Meyer, M., Wiederkehr, I., Panzner, M., Koldewey, C., & Dumitrescu, R.
(2022). A Reference Process Model for Usage Data-Driven Product Planning. Proceedings
of the 55th Hawaii International Conference on System Sciences, 6105–6114.
bibtex: '@inproceedings{Meyer_Wiederkehr_Panzner_Koldewey_Dumitrescu_2022, title={A
Reference Process Model for Usage Data-Driven Product Planning}, booktitle={Proceedings
of the 55th Hawaii International Conference on System Sciences}, author={Meyer,
Maurice and Wiederkehr, Ingrid and Panzner, Melina and Koldewey, Christian and
Dumitrescu, Roman}, year={2022}, pages={6105–6114} }'
chicago: Meyer, Maurice, Ingrid Wiederkehr, Melina Panzner, Christian Koldewey,
and Roman Dumitrescu. “A Reference Process Model for Usage Data-Driven Product
Planning.” In Proceedings of the 55th Hawaii International Conference on System
Sciences, 6105–14, 2022.
ieee: M. Meyer, I. Wiederkehr, M. Panzner, C. Koldewey, and R. Dumitrescu, “A Reference
Process Model for Usage Data-Driven Product Planning,” in Proceedings of the
55th Hawaii International Conference on System Sciences, 2022, pp. 6105–6114.
mla: Meyer, Maurice, et al. “A Reference Process Model for Usage Data-Driven Product
Planning.” Proceedings of the 55th Hawaii International Conference on System
Sciences, 2022, pp. 6105–14.
short: 'M. Meyer, I. Wiederkehr, M. Panzner, C. Koldewey, R. Dumitrescu, in: Proceedings
of the 55th Hawaii International Conference on System Sciences, 2022, pp. 6105–6114.'
conference:
name: 55th Hawaii International Conference on System Sciences
date_created: 2022-01-18T07:53:00Z
date_updated: 2022-02-21T10:40:35Z
department:
- _id: '563'
language:
- iso: eng
page: 6105-6114
publication: Proceedings of the 55th Hawaii International Conference on System Sciences
publication_status: published
status: public
title: A Reference Process Model for Usage Data-Driven Product Planning
type: conference
user_id: '77201'
year: '2022'
...
---
_id: '33707'
author:
- first_name: Maurice
full_name: Meyer, Maurice
id: '77201'
last_name: Meyer
orcid: 0000-0003-0606-7321
- first_name: Melina
full_name: Panzner, Melina
id: '72658'
last_name: Panzner
- first_name: Christian
full_name: Koldewey, Christian
id: '43136'
last_name: Koldewey
orcid: https://orcid.org/0000-0001-7992-6399
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: Meyer M, Panzner M, Koldewey C, Dumitrescu R. 17 Use Cases for Analyzing Use
Phase Data in Product Planning of Manufacturing Companies. Procedia CIRP.
2022;107:1053-1058. doi:10.1016/j.procir.2022.05.107
apa: Meyer, M., Panzner, M., Koldewey, C., & Dumitrescu, R. (2022). 17 Use Cases
for Analyzing Use Phase Data in Product Planning of Manufacturing Companies. Procedia
CIRP, 107, 1053–1058. https://doi.org/10.1016/j.procir.2022.05.107
bibtex: '@article{Meyer_Panzner_Koldewey_Dumitrescu_2022, title={17 Use Cases for
Analyzing Use Phase Data in Product Planning of Manufacturing Companies}, volume={107},
DOI={10.1016/j.procir.2022.05.107},
journal={Procedia CIRP}, publisher={Elsevier BV}, author={Meyer, Maurice and Panzner,
Melina and Koldewey, Christian and Dumitrescu, Roman}, year={2022}, pages={1053–1058}
}'
chicago: 'Meyer, Maurice, Melina Panzner, Christian Koldewey, and Roman Dumitrescu.
“17 Use Cases for Analyzing Use Phase Data in Product Planning of Manufacturing
Companies.” Procedia CIRP 107 (2022): 1053–58. https://doi.org/10.1016/j.procir.2022.05.107.'
ieee: 'M. Meyer, M. Panzner, C. Koldewey, and R. Dumitrescu, “17 Use Cases for Analyzing
Use Phase Data in Product Planning of Manufacturing Companies,” Procedia CIRP,
vol. 107, pp. 1053–1058, 2022, doi: 10.1016/j.procir.2022.05.107.'
mla: Meyer, Maurice, et al. “17 Use Cases for Analyzing Use Phase Data in Product
Planning of Manufacturing Companies.” Procedia CIRP, vol. 107, Elsevier
BV, 2022, pp. 1053–58, doi:10.1016/j.procir.2022.05.107.
short: M. Meyer, M. Panzner, C. Koldewey, R. Dumitrescu, Procedia CIRP 107 (2022)
1053–1058.
date_created: 2022-10-13T07:48:02Z
date_updated: 2022-10-13T07:53:45Z
department:
- _id: '563'
doi: 10.1016/j.procir.2022.05.107
intvolume: ' 107'
keyword:
- General Medicine
language:
- iso: eng
page: 1053-1058
publication: Procedia CIRP
publication_identifier:
issn:
- 2212-8271
publication_status: published
publisher: Elsevier BV
status: public
title: 17 Use Cases for Analyzing Use Phase Data in Product Planning of Manufacturing
Companies
type: journal_article
user_id: '77201'
volume: 107
year: '2022'
...
---
_id: '33706'
author:
- first_name: Melina
full_name: Panzner, Melina
id: '72658'
last_name: Panzner
- first_name: Maurice
full_name: Meyer, Maurice
id: '77201'
last_name: Meyer
orcid: 0000-0003-0606-7321
- first_name: Sebastian von
full_name: Enzberg, Sebastian von
last_name: Enzberg
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Panzner M, Meyer M, Enzberg S von, Dumitrescu R. Business-to-Analytics Canvas
- Translation of Product Planning-Related Business Use Cases into Concrete Data
Analytics Tasks. In: Procedia CIRP. Vol 109. Elsevier BV; 2022:580-585.
doi:10.1016/j.procir.2022.05.298'
apa: Panzner, M., Meyer, M., Enzberg, S. von, & Dumitrescu, R. (2022). Business-to-Analytics
Canvas - Translation of Product Planning-Related Business Use Cases into Concrete
Data Analytics Tasks. Procedia CIRP, 109, 580–585. https://doi.org/10.1016/j.procir.2022.05.298
bibtex: '@inproceedings{Panzner_Meyer_Enzberg_Dumitrescu_2022, title={Business-to-Analytics
Canvas - Translation of Product Planning-Related Business Use Cases into Concrete
Data Analytics Tasks}, volume={109}, DOI={10.1016/j.procir.2022.05.298},
booktitle={Procedia CIRP}, publisher={Elsevier BV}, author={Panzner, Melina and
Meyer, Maurice and Enzberg, Sebastian von and Dumitrescu, Roman}, year={2022},
pages={580–585} }'
chicago: Panzner, Melina, Maurice Meyer, Sebastian von Enzberg, and Roman Dumitrescu.
“Business-to-Analytics Canvas - Translation of Product Planning-Related Business
Use Cases into Concrete Data Analytics Tasks.” In Procedia CIRP, 109:580–85.
Elsevier BV, 2022. https://doi.org/10.1016/j.procir.2022.05.298.
ieee: 'M. Panzner, M. Meyer, S. von Enzberg, and R. Dumitrescu, “Business-to-Analytics
Canvas - Translation of Product Planning-Related Business Use Cases into Concrete
Data Analytics Tasks,” in Procedia CIRP, 2022, vol. 109, pp. 580–585, doi:
10.1016/j.procir.2022.05.298.'
mla: Panzner, Melina, et al. “Business-to-Analytics Canvas - Translation of Product
Planning-Related Business Use Cases into Concrete Data Analytics Tasks.” Procedia
CIRP, vol. 109, Elsevier BV, 2022, pp. 580–85, doi:10.1016/j.procir.2022.05.298.
short: 'M. Panzner, M. Meyer, S. von Enzberg, R. Dumitrescu, in: Procedia CIRP,
Elsevier BV, 2022, pp. 580–585.'
date_created: 2022-10-13T07:47:30Z
date_updated: 2022-10-28T09:35:39Z
department:
- _id: '563'
doi: 10.1016/j.procir.2022.05.298
intvolume: ' 109'
keyword:
- General Medicine
language:
- iso: eng
page: 580-585
publication: Procedia CIRP
publication_identifier:
issn:
- 2212-8271
publication_status: published
publisher: Elsevier BV
status: public
title: Business-to-Analytics Canvas - Translation of Product Planning-Related Business
Use Cases into Concrete Data Analytics Tasks
type: conference
user_id: '72658'
volume: 109
year: '2022'
...