=Paper=
{{Paper
|id=Vol-2016/paper12
|storemode=property
|title=Extracting Service Process Models with Teams
|pdfUrl=https://ceur-ws.org/Vol-2016/paper12.pdf
|volume=Vol-2016
|authors=Tuuli Klemetti,Reijo Salminen,Olli Martikainen,Riku Saikkonen,Eljas Soisalo-Soininen
|dblpUrl=https://dblp.org/rec/conf/simpda/KlemettiSMSS17
}}
==Extracting Service Process Models with Teams==
<pdf width="1500px">https://ceur-ws.org/Vol-2016/paper12.pdf</pdf>
<pre>
 Extracting Service Process Models with Teams

 Tuuli Klemetti, Reijo Salminen, Olli Martikainen, Riku Saikkonen, and Eljas
                              Soisalon-Soininen

                          Aalto University, Finland
             {tuuli.klemetti, reijo.salminen, riku.saikkonen,
        eljas.soisalon-soininen}@aalto.fi, olli.martikainen@pfu.fi



      Abstract. This work continues our former study on extracting service
      process models from location data. We extend our former work for teams
      of professionals working in the process. A model for the teams is required
      in our analytics for the optimization of team based processes.

      Keywords: Service process modelling, location-based, teams of people


1   Automated Process Modelling Approach
In the current study we extend our wireless process measurement approach [1]
for service processes where customers are served by teams. The teams include
complementary professionals, such as dentist, dental hygienist and dental assis-
tant. We call such teams as multiprofessional teams.




                    Fig. 1: An example of a modelled process


   In Figure 1 we present an example with three activities a1 , a2 and a3 in
corresponding locations with two Bluetooth beacons. The customers are marked
as blue and the service personnel as red, and they all carry a mobile receiver
when the process is measured. In activity a3 both persons u3 and u4 work




                                        147
together as a team. The resource allocation in teams plays an important role in
the analysis [2], which is described in [4–6].

2   The Measurement System
The measurement system is shown in Figure 2. The Bluetooth beacons are send-
ing periodically their identities, and the Smartphone application is collecting the
measurement RSSI values. The results are sent to the Server which calculates
the model. We tested the system and the analytical approach for process model
extraction in a laboratory case study based on typical real dental service centers
in the City of Tampere [3]. The original Tampere project was during 2013-2016.




                        Fig. 2: The measurement system




References
1. Ye Zhang, Riku Saikkonen, Olli Martikainen and Eljas Soisalon-Soininen: Location-
   Based Automated Process Modelling, SIMPDA 2016 (2016)
2. Halonen, R., Martikainen, O., Juntunen, K., Naumov, V.: Seeking efficiency and
   productivity in health care. In 20th Americas Conference on Information Systems.
   AMCIS-0251-2014.R1. (2014)
3. R. Halonen, O. Martikainen, S. Rsnen, M. Uusi-Pietil: Improved Dental Services
   With Process Modelling, The 11th Mediterranean Conference on Information Sys-
   tems (MCIS), Genoa, Italy, 2017, 1-15.
4. Naumov, V., Martikainen, O.: Method for Throughput Maximization of Multiclass
   Networks with Flexible Servers, ETLA Discussion Papers, The Research Institute
   of the Finnish Economy nro 1261 (2011)
5. Naumov, V., Martikainen, O.: Optimal Resource Allocation in Multiclass Net-
   works,ETLA Discussion Papers, The Research Institute of the Finnish Economy
   nro 1262 (2011)
6. Naumov, V., Martikainen, O.: Queueing Systems with Fractional Number of Servers,
   ETLA Discussion Papers, The Research Institute of the Finnish Economy nro 1268
   (2012)




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