The Slovakian Truck Company:
The company wants to improve lifetime of the truck tyres. Next to fuel consumption, consumption of tyres and their replacement represent according to them, one of most costly processes in company. Improvement effort was made through the use of PDCA cycle. It start with analysis of current state of tyres consumption. The variability of tyres consumption based on years and types was investigated. Then the causes of tyres replacement were identified and screening DOE was conducted. After a screening design, the full factorial design of experiment was used to identify main drivers of tyres deterioration and breakdowns. Based on result of DOE, the corrective action were propose and implemented.
(Source: QIP journal)
The prescription pricing agency is responsible for the processing of all prescriptions issued by General Medical Practitioners throughout England. They currently pay out over £6 billion per annum on behalf of HM Government with the help of nearly 1000 staff in 10 locations throughout the UK. With a general rise in the number of standard issues there is a constant need to update and improve efficiency by re-engineering the processing itself. Recently it was decided to split part of the data input and pricing work into a number of sub-processes, leading to a more rationalised approach. In the new structure, it was thought that some of the simpler more repetitive tasks such as initial entry and logging of prescriptions, could be performed by less experienced staff, including new recruits. The Authority needed to know thatdata input accuracy levels could be improved or at least maintained while speeding up the input of vast volumes of data, despite the intention to use less experienced staff. It was hoped that the more experienced staff could be reserved for more complex tasks.
An experiment was designed with two three level factors being
- the level of experience of staff: experienced, semi-experienced or novices
- the instructions given: go fast, go fast and be accurate, be accurate
Nine trials were planned with all 9 combinations of instructions and experience. The measured outputs were
a) speed measured as number of inputs per hour averaged over a day and
b) accuracy measured as proportion of inputs without an error. The trial lasted for 5 days. It was noted that accuracy rates remained constant over the trials and there was no relationship between speed and accuracy.
It was found that speed and accuracy increase with experience level. Accuracy is not particularly affected by the instructions given. Speed is affected by the instructions given. If a person is asked to ‘go fast’, they will go faster and it will not tend to affect ,their accuracy level, at least as possible to measure in this fairly small experiment. However, if they are asked to be accurate, speed will reduce, without any noticeable effect on accuracy. It is therefore best to ask for faster speeds and accuracy levels will hold their ‘natural’ level for the person. Multiple regression models were derived for speed and accuracy. These could be used to give predictions of expected outcome for any combination of instructions and experience. The predictions can be used to set reasonable performance standards against which to compare new starters
(Source: Pro enbis)