**6. Discussion**

know that we have 0.8 by the Likert non‐optimal knowledge alignment. If the times in this table were measured without being aware of this knowledge gap then the real throughput time is longer. In a real case, we could measure this by comparing the knowledge gap and the difference between planned and real production times (we have to exclude other causes for time extension first). In our demonstration, we assumed that every 0.1 of knowledge

gap adds 1% to planned process throughput time.

**Table 4.** Production parameters of As‐Is process.

232 Knowledge Management Strategies and Applications

The main specialty of our model is that we permit changes of the process because the actual knowledge is not appropriate for it. However, we do not allow changes in the sequence of activities; we allow only changes in the sequence of using employees. The results are new partial activities in the process; consequently, the process workflow is jumping forwards and backwards between employees.

In our model, we removed all unnecessary knowledge from the work positions that were process 'bottlenecks' and replaced it with the new process structure; this was done by taking into consideration the availability of the actual knowledge of employees. The entire indi‐ vidual employee time capacity is now focused only on the utilization of knowledge that is bottlenecked. Other required knowledge in the process that is also present in other employ‐ ees is removed from that work position. Employee capacity is now free of all non‐bottleneck knowledge, and this raises its capacity availability.

In our simulations, we used process time indicators to verify our assumption, even if we know, on the basis of real projects [31, 32], that the best improvements in the ETO production are achieved on the process quality indicators. Time indicators are improved indirectly as a result of better product quality: fewer aftermarket repairs means less additional invested time in the total production time of the specific product. The starting point of all scenarios is the departure of one employee from the original process (Scenario 0). In Scenario 1, we reacted by implement‐ ing the lean manufacturing principle of capacity balancing: the work of the lost employee is divided among remaining employees on the basis of capacity levelling without additional work position breaks. This is a common management decision, and it is expressed as a load capacity per shift (%) indicator in **Table 4**. This decision produced the knowledge gap of 1.7 (**Table 3**).

In Scenario 2, we used our model with the activity‐cutting principle, and we reduced the knowledge gap by 0.4 or 23.5% (**Table 3**). Most time indicators were also improved (**Table 5**), except for the unbalanced load capacity per shift (%) indicator, and a lower process through‐ put rate (from 9 to 8 products per shift). Both indicators would have negative impact in mass or serial production, but according to the requirements of the ETO production it is more important that we achieved the desired quality of knowledge for production process because there are no repetitions (rather only unique, one‐time process executions). Management can balance these indicators and make the decision that is adopted for a specific process 'case'.

In Scenario 3, we tested the total ignorance of the Lean Manufacturing principles, and we performed additional activity cuts for searching for even better knowledge alignment. We did not achieve a lower knowledge gap (**Table 3**); we also worsened all time indicators according to Scenario 2 (**Table 5**). This indicated that there is a point in the repetition of activity‐cutting procedure after which the process becomes so inefficient that is better to hire a new employee if the knowledge gap is still too high for achieving the appropriate quality of ETO products. Where that point is, what the gap should be and whether its value is of universal use or case sensitive are all subjects of future research.


**Table 5.** The impact of activity‐cutting principle on production parameters in scenarios from 1 to 3.
