5.1 Donaldson Loop
5.1.6 Review: Understanding the Line
With the installation successful, output stats from the line trickled in slowly.
A definite improvement with regards to the overall line rate was noted with the added requirement of one-piece-flow now finally realised.
Teething issues
New machinery, generally prototypes that are, as yet untested by industry, struggle to be integrated to a team used to other practices without effective guidance and training. The seed team must be able to make a call when it comes down to this learning curve.
Understanding the process is crucial to know when to push and when to allow the team to grow and develop ownership of their own understanding of a concept.
Set-up procedures with new machinery are also tricky. The first weeks working with new machinery, before TPM was introduced, required the team to familiarise themselves with the systems. It followed that machine specialists on the line had the added advantage over normally trained operators during set up. The introduction of standard work instructions (SWIs) and training reduced this significantly.
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Loop KPIs
Apart from achieving one-piece-flow, some loop performance metrics are reported below.
Table 6 compares the planned downtime for the implementation loop to the actual downtime recorded. Effective planning of this stage allowed the team to be ahead of schedule and able to react to any issues arising during the loop’s implementation.
Table 6: Unplanned vs. Planned Maintenance Downtime
Allowable Unplanned Downtime Actual Unplanned Downtime 16 hours (2 shifts) 3.5 hours
Maximum Planned Downtime Actual Planned Downtime
40 hours (5 shifts) 24 hours (3 shifts + weekend overtime)
The line rate can be seen in Table 7. While the numbers have been rounded up, it is apparent that the rate/hour increased in terms of planned output as well as actual. The similar daily outputs can be attributed to the fact that the line was now required less hours per shift to run the same daily output.
Table 7: Table Showcasing the Line Comparison between the EDF and the Dispense
EDF Dispense
Daily Output 1000-1100 1000
Rate/hour Planned 120 170
Rate/hour Actual 57 98
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See Figure 13 for EDF data during March/April and Figure 14 for dispense data during October/November.
Figure 13: Sample of EDF Production Figures
Figure 14: Sample of RSL Production Figures after Dispense Installation
It should be noted that the projected target for the new dispense is more in line with the actual output reached by the machine when compared to the EDF data. The EDF suffered 73
from extremely long lead times for spare parts, when they failed, the spares coming in to replace them were still in the process of being shipped.
The expected OEE of the installed system compared favourably to the EDF data as shown in Figure 15 and Figure 16 respectively on page 75. The average OEE for the EDF hovered between 0.6 and 0.8, compared to the installed system running at an improved range between 0.8 and 1.2. The OEE calculation was modified to take the two rates that the dispense operated at into account.
Further Data for the RSL over the course of the loop period can be seen in Appendix E: Loop Data.
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Figure 15: Graph of the OEE for the EDF during a Sample Period
Figure 16: Graph of the OEE for the Dispense During a Sample Period
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In terms of this improvement loop’s scope set out in the analyse phase, the loop has satisfied the required outcomes which included creating one-piece-flow, integrating the pleater into the new upstream area and increases in the overall line performance, with favourable OEE figures for comparison.
Observations
These were observations during the implementation and the sustainability review phases that were beyond the loop scope, but needed to be seen to. In these cases, the champion must determine whether the current scope was relevant or whether a new improvement loop needed to be created to solve them within a redefined scope.
Set up times were extravagant, but this was attributed to the shop-floor staff familiarising themselves with the machinery, it was hoped that this would diminish over time.
The upstream area also struggled with the pleat speed coming off the pleater. Where before, the pleater ran uncoupled to the line and the seam-seal station was able to work at its own pace and define their output, this was no longer the case. The pleater no longer had an inventory buffer that it could run into should there be processing issues further down the line. Upstream now needed to supply at the rate the dispense consumed packs; once the pleater had acclimatised itself to the required rate, this issue subsided with time.
It was made apparent by the seed team that there was no designated system to control improvement projects. There was no information available or system present when it came down to tracking how improvements were done and how they were documented to showcase any quality or process improvements.
Interference
Management often had a disturbing side view of issues and would circumvent previous statements and move other pieces into play to either limit or indirectly cause chaos unintentionally or without the full understanding of the effect. For example, a new demould machine had been commissioned for end-of-line, this had been done beyond the seed team’s knowledge, so when it came down to changing the end-of-line, there was a new requirement to integrate an untested prototype as a replacement process.
When push comes to shove, should all the information not be available to the implementation team at the planning stage, the entire project can be derailed by being
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forced to implement an unplanned-for strategy with low priority due to lack of information or it being missed by the team. Full disclosure and transparency needs to be understood by the management team and a scope must be signed off on to ensure this cannot take place.
The team was able to adapt accordingly by being ready and flexible to the changing conditions, and by being aware of all of the affected processes and implementation changes upfront that this required.