Abstract:
This report addresses productivity shortcomings at PG Aluminium (PGA) and how to address
those challenges in order to improve productivity. The main problem identi ed through a business
process analysis (BPA), value analysis, and work sampling study was labour productivity,
as the factory's productivity is at a low of 36% when the time spent on value-adding activities
are measured against the total time available for production. Productivity shortcomings can be
addressed by implementing Lean principles, such as the Just-In-Time (JIT) tool and techniques.
JIT, amongst other things focus on the elimination of wastes. Seven wastes are classi ed by JIT
of which three of the seven were identi ed in PGA's factory. They were \waiting", \unnecessary
motion", and \unnecessary inventory". These three wastes are directly hampering the
ow of
the fabrication process. The three wastes are therefore addressed in this study by improving the
current hardware picking process, as well as through the design of a scheduling model to increase
the
ow of the process in the factory. The hardware picking process was analysed in more depth
by doing a Business Process Analysis (BPA) which highlighted areas for improvement in the
picking process. A simpli ed version of the scheduling model was designed using linear modelling
principles and Python software. The model aims to produce as many products as possible
in the shortest amount of time. Using the time study data collected for the scheduling model,
a hypothetical individual performance measurement tool (IPMT) was designed that PGA can
use to compare worker performance to expected performance. The aim is to improve the overall
ow of the factory by improving labour productivity so that ultimately the business process can
be optimised.
The scheduling model, hardware picking process improvement suggestions, and the IPMT were
evaluated using the evaluation methods suggested by Manson (2006). Amongst other evaluation
methods, a simulation model was designed using simulation software (AnyLogic), which was
used to evaluate the e ect of the scheduling model and the hardware picking process improvements.
From the analysis it was determined that the overall hardware picking time can decrease
by up to 33.8% if the suggested improvements are implemented. The results of the scheduling
model using the simulation indicates that the overall dead time (which translates into work-inprogress)
can be reduced by 46.3%.
From this study it is therefore clear that the overall productivity at PGA can be improved
by implementing an improved hardware picking system as well as a scheduling model.
