ImagesMagUK_February_2021

www.images-magazine.com FEBRUARY 2021 images 39 KB BUSINESS DEVELOPMENT inks. Also ‘time per set-up’ was relevant as it allowed us to create daily maps for the operator workflow. For example, if we knew that the average number of screens was eight, the average number of flashes was four and the average time for set-up was 40 minutes, we could provide the operator with a map for the day based on 40 minutes per set-up. We could also provide staging areas for flashes, therefore reducing the need to chase down peripheral equipment. Set-up The ‘set-up’ in Factory A is defined as the time it takes from touching the first screen (the first action in a planned map) to handing quality control (QC) a finished printed garment. If QC rejected the garment for a printing error, the set-up time continued. If they rejected the garment because of an art or screen error, the style would start over and add to the ‘per article rate’ as a negative. If they rejected the style for aesthetic reasons, it moved instead to the QC report. Per screen set-up Next was ‘per screen set-up’. This is important, because if a factory is running a long average, for example 15 minutes per screen, you can set a target of reducing it to 14 minutes per screen, which is easy to achieve and reward. When employees are working towards hitting their targets, they need incentivising. It is easiest to use the ‘per screen set-up’ when creating goals rather than going for the ‘time per set- up’, because it is harder to get a handle on whether you’re reducing the average set-up time when you’re working on lots of different jobs with varying screen numbers – you will only discover that later when you look at the data. The ‘time per screen’ measurement is much more straightforward to keep track of while working. Special effect screen average We measured the ‘special effect screen average’ next. This is important because it identifies the anomalies and helps you see where there may be some ineffeciencies. Typically, effects take longer to set up than flat inks. The data collection might show you that one operator is much faster at setting up and getting approvals on high-density printing, which creates an opportunity to arrange for them to train a colleague who is losing time in this area. It also provides reasons for ‘out of range’ set- ups and helps stabilise the data. Set-up materials Finally ‘set-up materials’, ie how many pieces were used to set up the job. This is ultimately tallied against waste data. The need to incentivise When Factory A was initially analysed, the set-up times were 48 minutes per screen. Over a two-year period of data and analytics, this was reduced to nine minutes per screen. We also reduced the 158 set-up pieces per job to a standard 32 pieces per job – or one round on the oval machine (the set-up materials are essentially scrap materials used to start a printing process, and were not considered waste). It is important to note that Factory A’s results were supported using incentives and positive reinforcement. If the team achieved goals, they would win meal tickets to the cafeteria. We only gave incentives to teams, we never gave them to individuals unless there was a contest (and contests took place during training scenarios only). So, for example, to win a meal ticket in samples the team had to produce three set-ups per day per press that was operating in samples. Every day they completed the target, they would win a meal ticket for the team. They could only win if they completed the entire sample season on time with aesthetic efficiency – the team never lost after the programme started. Be prepared to follow through with the rewards and be proud to provide these incentives to your staff. The goals were always small steps that were easily achievable. If teams showed challenges achieving their goal, we created training based on the data collected. An example of this is that at one point there was an issue with aesthetic being rejected by QC during samples. It was happening frequently and causing delays. So, we held a contest. The printers received rewards individually for achieving several aesthetic and hand- feel challenges – as a result, overall efficiency improved. Inventory data included ‘blanks for set- ups’, ‘printed reorders’, and ‘per article rate’. The ‘per article rate’ is essentially an efficiency rating that shows how many times a style is set-up before it is produced. After the initial training, the factory performed at a 2.0 per article rate. After efficiencies and corrections were implemented, the factory achieved 1.15 per article rate. Using a linear regression model, we were able to anticipate sample orders and reduce the per article rate, and we used pre-printed inventory to minimise multiple set-ups and to reduce cost. We then added any excess inventory into production orders at the end of the season. The data sets The factory created a ‘B’ grade report with 25 data points related to product failure during QC (B grade = non-retail product). We gave each line item a percentage of failure to contribute to There are six unique data points that contribute to the success of Factory A, and each has a percentage of weight in the overall outcome The set-up per style was just 1.15 by the end of the programme, down from 1.97