Page 263 - Special Topic Session (STS) - Volume 2

STS490 Riaan d.J.
to a specific problem posed by industry. The student has to complete the
project over a six-month period with the assistance of an academic supervisor
(responsible for academic quality) and a client project officer (responsible for
business value add). Formal on-site project meetings are scheduled where all
role players should be present to discuss progress. In this way the academic
supervisors also gain industry experience and get a feel for the problems being
faced by industry. This often leads to industry directed research projects by
the supervisor for the company. The company has the benefit of screening the
student for employment and potential problem-solving value add at relatively
low cost. The demand for these students has increased dramatically,
supported by the fact that project proposals outnumber available students 2:1.
As a fringe benefit the programme has spurred a number of research
imperatives between academia and industry and many papers have already
been submitted, which have been co-authored by academics and
practitioners. It should be noted that although student projects are classified
confidential and although students have signed non-disclosure agreements
with the assigned company, it is amazing how quickly client project officers
can share sensitive information when they are offered co-authorship of a
paper. Interestingly, but not surprisingly, alumni of this programme become
future client project leaders and research collaborators.

Please see the references for more information about this programme.

References
1. Coetzer, R.J.L. & De Jongh, P.J. 2016. Discussion of ‘industrial statistics:
the challenges and the research’. Quality engineering, 18(1):63-68.
2. De Jongh P.J. & Erasmus, C.M. 2014. Industry-directed training and
research programmes: the BMI experience. S African journal of science,
110(11/12), Art. #2013-0392, 8 pages. http://dx.doi.org/10.1590/
sajs.2014/20130392
3. 3. De Jongh, P.J. 2018. University-industry engagement in data science.
https://www.youtube.com/watch?v=QgsoRUulLhU [Video].
4. Meng, 2018. Statistical paradises and paradoxes in big data. The annals
of applied statistics, 12(2):685-726.

252 | I S I W S C 2 0 1 9