Gezani Richman Miyambu
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Numeric measurement of business process optimization
Gezani Richman Miyambu , Solly Matshonisa Seeletse doi: http://dx.doi.org/10.21511/ee.07(4).2016.02The paper describes a simple, straightforward method to measure progress of business process optimization (BPO). The aim is to derive measures of the degree of BPO attainment in order to identify future priority focus for ensuing exercises. These measures can help to identify components of business that should be improved towards full optimization of processes in business. In an ideal case of the business containing all the components, a large business scenario is assumed. However, flexibility is permissible when changes are experienced with either some business aspects missing or new ones added. A measure of BPO progress was eventually developed based on these circumstances. A BPO measurement is described for presentation as a percentage or proportion.
Keywords: BPE, BPO, change management, measure, risk management, success factor.
JEL Classification: C1, C3, C5, C6, O3 -
Centralized Statistics Courses at SMU: opportunity and advantage for SOR; research benefits for SMU
Gezani Richman Miyambu , Maria Mokgadi Lekganyane , Solly Matshonisa Seeletse doi: http://dx.doi.org/10.21511/ee.08(4).2017.05Environmental Economics Volume 8, 2017 Issue #4 pp. 37-43
Views: 678 Downloads: 130 TO CITE АНОТАЦІЯThe Department of Statistics and Operations Research (SOR) at the Sefako Makgatho Health Sciences University (SMU) in South Africa offers courses in Statistics (Stats). Several departments in SMU campus require Stats training in their study program. In the interest of quality offering in Stats training and for statistical services needed for research, SOR oofers to collect, centralize and facilitate all the Stats modules on campus. This paper then reflects on the impressions of academics and researchers on SMU campus regarding their view on centralized Stats courses in SMU. This will help explore the opportunities, envisaged research benefits and challenges for centralizing all the SMU Stats training in SOR.