**3.3. Linking knowledge optimization and work position breaks**

From the perspective of real business in ETO production, especially in this time of global economic crisis, accessibility to newly required knowledge is greatly limited due to extra educational costs. Downsizing also means that processes must be executed with fewer employees but at the same time the level of product quality must remain equal to previ‐ ous process executions. Management typically reacts with reorganization of employees on activities. Furthermore, because we cannot split 'the human body', his or her structure of knowledge and the time capacity of that knowledge cannot be optimal for current (ideal) process. In the theory, the problem can be easily solved if we have all current employees with all required knowledge of the process.

In ETO production, there are many specialists (e.g. electrical engineers, mechanical engineers, software engineers) with one or two dominant fields of knowledge of very high quality or strength, and few employees with wide spectra of high quality knowledge (senior engineers, mechatronics), because the latter are too expensive. However, they are also key employees for the ETO production; they have the big picture over each new product, and they can control the efficiency and quality of the overall production process. They are never 'bottlenecks' in the pro‐ cess with regard to knowledge, but they can be problematic with regard to the available time capacity of his/her specific required knowledge, because they are involved in many processes (ETO projects).

This phenomenon is also a result of the accumulation of many small organizational changes in processes over time. When the company was established (or after process re‐engineering project), processes and work positions were optimally designed for execution, employees were carefully selected and their knowledge was appropriate for knowledge requirements of work positions (**Figure 2**).

Over time, new activities were slowly added to work positions, thus generating newly required knowledge. These changes were so small at the beginning that the management did not recognize them as knowledge problems or capacity problems. They had no effect on the employees except that the work position received one or two new key pieces of knowledge that employees had to obtain. After a few years of small changes, the work position and their key knowledge structure had expanded in such a way that the management and the employee did not know which pieces of knowledge of the work position were key for busi‐ ness success (e.g. a designer in ETO production is working 30% of his capacity on designing, 40% of the time he is occupied with routine paper work and another 30% he is attending meetings; if we require 100% design work, then this person's design knowledge is a capacity bottleneck).

**Figure 2.** Explanation of cutting activities when employee leaves the process.

In practice, poor work quality can be found in the process due to inappropriate knowledge alignment. This generates additional feedback loops, activities are repeated and the result is additional work position breaks. Determining the causes of additional activity breaks is not a

From the perspective of real business in ETO production, especially in this time of global economic crisis, accessibility to newly required knowledge is greatly limited due to extra educational costs. Downsizing also means that processes must be executed with fewer employees but at the same time the level of product quality must remain equal to previ‐ ous process executions. Management typically reacts with reorganization of employees on activities. Furthermore, because we cannot split 'the human body', his or her structure of knowledge and the time capacity of that knowledge cannot be optimal for current (ideal) process. In the theory, the problem can be easily solved if we have all current employees

In ETO production, there are many specialists (e.g. electrical engineers, mechanical engineers, software engineers) with one or two dominant fields of knowledge of very high quality or strength, and few employees with wide spectra of high quality knowledge (senior engineers, mechatronics), because the latter are too expensive. However, they are also key employees for the ETO production; they have the big picture over each new product, and they can control the efficiency and quality of the overall production process. They are never 'bottlenecks' in the pro‐ cess with regard to knowledge, but they can be problematic with regard to the available time capacity of his/her specific required knowledge, because they are involved in many processes

This phenomenon is also a result of the accumulation of many small organizational changes in processes over time. When the company was established (or after process re‐engineering project), processes and work positions were optimally designed for execution, employees were carefully selected and their knowledge was appropriate for knowledge requirements of

Over time, new activities were slowly added to work positions, thus generating newly required knowledge. These changes were so small at the beginning that the management did not recognize them as knowledge problems or capacity problems. They had no effect on the employees except that the work position received one or two new key pieces of knowledge that employees had to obtain. After a few years of small changes, the work position and their key knowledge structure had expanded in such a way that the management and the employee did not know which pieces of knowledge of the work position were key for busi‐ ness success (e.g. a designer in ETO production is working 30% of his capacity on designing, 40% of the time he is occupied with routine paper work and another 30% he is attending meetings; if we require 100% design work, then this person's design knowledge is a capacity

**3.3. Linking knowledge optimization and work position breaks**

with all required knowledge of the process.

subject of this research.

224 Knowledge Management Strategies and Applications

(ETO projects).

bottleneck).

work positions (**Figure 2**).

For such cases, we created a process and knowledge algorithm that is connected with a Key performance indicators (KPI) that measures process corruption as follows:


Such a reorganized process is reengineered on the basis of knowledge.

**Figure 3.** Possible outputs of algorithm for optimal knowledge alignment in ETO production.

### **4. Input data**

### **4.1. Processes, process activities, work positions and required knowledge**

In ETO production, at first sight, almost every product has its own and unique production process (routing). The fact is that activities (operations) among different processes are almost the same with regard to required knowledge. They differ mostly in the time required for exe‐ cution. Because each product has its unique structure (bill of material), the process is named in practice as a project and its operations are named as activities. However, from the top‐down approach, each project in ETO production has almost the same set and the same sequence of project phases (with many sub‐activities), for example, (1) preparation, (2) design, (3) con‐ struction, and (4) testing. Therefore, it can be assumed that we have a standard form of the process (with activities) for almost all new products.

The same process activity could appear in a structure of many different processes and it is usually performed by the same work position (e.g. the same quality control activity with the same control parameters and tools for the whole product group). Moreover, one work posi‐ tion executes many activities. Until the system is well organized, a work position aggregates activities with approximately the same required set of knowledge. We defined that the required knowledge of a specific work position is represented as a set of knowledge from all executed activities. The sets of required knowledge of specific activity and their strength (Likert scale from 1 to 5; 5 meaning very important) are defined by the company's internal and external experts. If a specific piece of knowledge is required for the execution of many activities, the model uses its maximal value as a required strength.

Complex work positions have a wide range of required knowledge, many of unimportant strength. Reducing the amount of various required knowledge can simplify the calculations. Simplification was achieved with the definition of key knowledge *Kk* for each work position. If the strength of specific knowledge is above a specific level, it is treated as key knowledge of that work position.

In practice, the above‐described idea of capturing process activities and their required knowl‐ edge can be used for documenting As‐Is processes and, more importantly, for predicting future products, To‐Be processes and their expected required knowledge. This is of great importance for planning required knowledge of future ETO production. We can analyse the following:


**4. Input data**

226 Knowledge Management Strategies and Applications

**4.1. Processes, process activities, work positions and required knowledge**

**Figure 3.** Possible outputs of algorithm for optimal knowledge alignment in ETO production.

process (with activities) for almost all new products.

In ETO production, at first sight, almost every product has its own and unique production process (routing). The fact is that activities (operations) among different processes are almost the same with regard to required knowledge. They differ mostly in the time required for exe‐ cution. Because each product has its unique structure (bill of material), the process is named in practice as a project and its operations are named as activities. However, from the top‐down approach, each project in ETO production has almost the same set and the same sequence of project phases (with many sub‐activities), for example, (1) preparation, (2) design, (3) con‐ struction, and (4) testing. Therefore, it can be assumed that we have a standard form of the

The same process activity could appear in a structure of many different processes and it is usually performed by the same work position (e.g. the same quality control activity with the same control parameters and tools for the whole product group). Moreover, one work posi‐ tion executes many activities. Until the system is well organized, a work position aggregates


If we have proper data on all the above mentioned entities (processes, activities, work posi‐ tions, knowledge requirements with required strength) for the present time, and if we have good knowledge requirements (definitions) of new products (especially required technol‐ ogy and activities), we can then simulate all future knowledge requirements in advance. Therefore, we can determine differences, for example, which work position must be knowl‐ edge‐reconstructed in the future; consequently, we can define projected mandatory changes in a structure of actual knowledge (employees).

### **4.2. Employees, actual knowledge and knowledge gap**

Employees represent the basis for gathering current knowledge. There are many approaches to prove that an employee possesses specific knowledge and what the quality of it is (strength, level). In our approach, the 360° feedback method [28] was used. We used a list of all key required knowledge and assessed all employees (Likert scale from 0 to 5; 0 means knowledge not available). We gave employees the opportunity to extend this explicit knowledge with their tacit knowledge. In the context of our model, the term 'tacit' means the knowledge of an employee that is currently unknown to the company. Knowing about tacit knowledge is essential information when new processes have requirements for new types of knowledge. In practice, for optimization, it is also recommended that we have the knowledge data about potential candidates for employees.

The last step of input data preparation is a calculation of the key knowledge gap: each employee is compared to all work positions. We used the criterion *c*ij, explained in Eq. (2). Any deviation of actual knowledge over and below the required knowledge is considered to be inappropriate and will lower process efficiency (**Table 1**).

**Table 1** shows a numerical example of matching the actual knowledge from *k*1 to *k*10 of employee *E*1 on activities from *a*1 to *a*7 of work position *W*1 (e.g. Product Manager of ETO project). The example is based on the real data of ETO company, Iskratel. Negative values (grey cells) represent deficits of employee knowledge strength compared to the required knowledge of a work position. The top rows represent activities of the work position with a


**Table 1.** Matching required and actual knowledge.

sum of negative values. We can identify activities that the employee is not suitable to execute (e.g. *a*1*, a*2*, a*3). The left column represents the required knowledge with the sum of negative values. We can identify the lack of employee knowledge (e.g. *k*4*, k*8).

In practice, we could integrate in our model the effect of learning and forgetting knowledge over time (decreasing knowledge strength if employee is not using that type of knowledge in processes for a long time). Because of model simplicity, this was not a subject of this research.
