**5.1. Input data of simulation scenarios**

We prepared four simulation scenarios as follows:


We can observe the things as follows:



**Table 2.** Input data of simulation scenarios.

types of knowledge for *w*<sup>1</sup> . We moved from *w*<sup>2</sup> to *w*<sup>3</sup> only knowledge *K*<sup>4</sup> , because the newly required strength is below the current required strength so it remains as it was for *w*<sup>3</sup> .

(iii) In scenario 3, the new activity cut did not cause any change in knowledge requirements (and strength) of *w*<sup>1</sup> and *w*<sup>3</sup> according to scenario 2.

### **5.2. Simulation results**

We can see in Scenario 2 (implementing activity‐cutting principle) that we decreased the knowledge gap in Scenario 1. Now, we must 'merge' the results of optimal knowledge align‐ ment to determine the impact of using the activity‐cutting principle on classic production optimization parameters (Scenario 3). Otherwise, we will break some lean manufacturing principles, for example, work balancing or eliminating waiting times. We added additional input data of As‐Is process in **Table 4**.

Knowledge‐Based Assignment Model for Allocation of Employees in Engineering‐to‐Order... http://dx.doi.org/10.5772/intechopen.70073 231


**Table 3.** Simulation results.

types of knowledge for *w*<sup>1</sup>

230 Knowledge Management Strategies and Applications

**Table 2.** Input data of simulation scenarios.

input data of As‐Is process in **Table 4**.

and *w*<sup>3</sup>

(and strength) of *w*<sup>1</sup>

**5.2. Simulation results**

for *w*<sup>3</sup> . . We moved from *w*<sup>2</sup>

newly required strength is below the current required strength so it remains as it was

(iii) In scenario 3, the new activity cut did not cause any change in knowledge requirements

We can see in Scenario 2 (implementing activity‐cutting principle) that we decreased the knowledge gap in Scenario 1. Now, we must 'merge' the results of optimal knowledge align‐ ment to determine the impact of using the activity‐cutting principle on classic production optimization parameters (Scenario 3). Otherwise, we will break some lean manufacturing principles, for example, work balancing or eliminating waiting times. We added additional

according to scenario 2.

to *w*<sup>3</sup>

only knowledge *K*<sup>4</sup>

, because the



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

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.
