LAPSE:2023.34019
Published Article
LAPSE:2023.34019
Improving the Process of Developing New Services Using Uncertain Data
Ilona Skačkauskienė, Povilas Švogžlys
April 24, 2023
The rapid changes in the services sector encourage companies to improve the processes for developing and delivering new services. An effective new service development process is not possible without planning. Business entities can plan their time, organise activities, and prioritise tasks according to the complexity or the total duration of the process. However, in the context of new service development, the planning aspect is still not sufficiently emphasised. It is noted that the aim is often to identify the factors that could reduce the duration of this process. However, there is a lack of relevant data based on certain expressions of time. The lack of such research limits the improvement of the planning processes for new service development projects. In order to evaluate the possibilities of application of the proposed new service development model for companies providing services, an empirical evaluation was conducted, using methods of expert survey, program evaluation and review, and Monte Carlo simulation. Guidelines for further research were formulated based on the obtained results.
Keywords
model, Monte Carlo, new service development, new service development model, NSD, PERT, three-point estimation, uncertainty
Suggested Citation
Skačkauskienė I, Švogžlys P. Improving the Process of Developing New Services Using Uncertain Data. (2023). LAPSE:2023.34019
Author Affiliations
Skačkauskienė I: Department of Management, Faculty of Business Management, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Švogžlys P: Department of Management, Faculty of Business Management, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania [ORCID]
Journal Name
Energies
Volume
14
Issue
16
First Page
5086
Year
2021
Publication Date
2021-08-18
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14165086, Publication Type: Journal Article
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LAPSE:2023.34019
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doi:10.3390/en14165086
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Apr 24, 2023
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