**3. Method**

employee wages and maximize strategic gains of the company from the increment of desirable competencies. The authors used a genetic algorithm as the optimization method. Competencies are also used in a model that seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub‐problem, the scheduling and staff assignment for a candidate set of selected projects is also optimized [16]. The authors used non‐linear mixed‐integer program formulation for the overall problem and then proposed heuristic solution techniques composed of a greedy heuristic for the sched‐

Recent studies are showing that the worker assignment problem is still important subject of research. Grosse et al. [17] designed a framework for integrating human factors into plan‐ ning models. Crawford et al. [18] showed application of worker assignment problem in proj‐ ect scheduling and they innovated optimization approach using hyper‐cube framework. A similar problem that discuses assignment of health care staff to tasks using fuzzy evalua‐ tion method was presented by Mutingi et al. [19]. Olivella et al. [20] gave emphasis on the cross‐training goals, while Senjuti et al. [21] optimized the assignment of tasks to workers by proposing efficient adaptive algorithms. Current efforts are dealing with additional variables in creating the perfect optimization framework (knowledge, cross‐training, etc.), or in finding the best optimization algorithms for solving worker assignment problem. They still assume that tasks are allocated to workers as 'they are'. Our effort was to study the effect of task redef‐ inition in the meaning of splitting tasks on smaller parts with the goal of better knowledge alignment. From the organizational view, especially when the creative job must be done (like in ETO companies), the list of required tasks is created according to the available knowledge of workers, and the new definition of tasks is a subject of optimization output. This was our

uling and staff assignment, and alternative 'meta' heuristics for the project selection.

main theoretical issue that is described as real business example as follows:

sumed that the quality can be reached only with proper knowledge.

agement will not approve recruiting new employees.

• The quantity of process output may be reduced.

company.

220 Knowledge Management Strategies and Applications

• At first, there is an optimal worker assignment on the work position requirements of ETO

• Then, one or many workers leave the company at their own initiative. Because of the high level of customer demand, there is no time to re‐educate the existing employees, and man‐

• The quality of process output (product) must remain at the same quality level. It is as‐

This is a typical example of a company that needs to increase the use of its internal sources. Many cases have been found in practice in ETO companies in which the management solved the problem of outgoing knowledge with reorganization of internal employees rather than with the simple extension of employees' existing capacities, for example, overtime work [22]. We also set two assumptions that were not subjects of this research: first, we accepted that in ETO production, business processes are constantly changing and, therefore, knowledge requirements are also changing. Second, because these are simulations, the relation between knowledge and the process efficiency was accepted: if employees have proper knowledge for The key solution of adjusting processes to the current knowledge lies in the theory of business process management [24], in which the main problem of achieving a short process through‐ put time lies in the waiting times among different work positions that are the consequence of unbalanced work. This problem is insignificant if the entire process is executed by only one employee who occupies one work position, because there are no work position breaks [25]. This works only in small companies. Large business systems are complicated: they have many business processes with diverse knowledge requirements (e.g. ETO production) and require many employees with different types and levels of knowledge. Work is divided into activities between different work positions. Each work position has its own knowledge requirements. In this case, management needs control over the specific knowledge and over the number of the work position changes, and must keep them at the 'desired' minimum level so that the optimal process efficiency and the work balance are reached. The problem is also in the required and actual capacity of the specific knowledge. The process output quantity reflects the frequency of activity executions [26]. From a previous description of the principle of minimization work position breaks, when the capacity of one employee is exceeded, an additional employee who can perform all activities in the process is required. Such a broadly educated employee is too expensive, and this solution is thus irrational. Therefore, the process is divided into activities (tasks) among many work positions with the least expensive employees. Management creates work positions with a simple and complex knowledge structure. However, dividing work in too many work positions slows down the process: the throughput time is extended because of the additional waiting time each time the work position is switched.

Regarding the theory of work position breaks, work position knowledge structure and employee knowledge capacity, we modified our previously published model [22]. **Figure 1** shows the steps of upgraded conceptual model. In the new model, we are measuring the effect of the partial corruption of a perfect process regarding better current knowledge align‐ ment from the perspective of employee capacity load and from that of process efficiency; with corruption of the process, we are decreasing its efficiency due to new additional work position breaks, but with better knowledge alignment we are again increasing the process efficiency.

### **3.1. Measuring optimal knowledge alignment**

We can observe in practice that if the current knowledge deficit is below the required knowl‐ edge, the result is less efficient work. Surprisingly, even an excess of actual knowledge over

**Figure 1.** Knowledge‐based assignment conceptual model.

the required level of knowledge has the same result of over‐educated and intelligent employ‐ ees becoming bored when they are executing routine activities [22]. Therefore, we modified a classic assignment linear integer problem of Kolman and Beck [3]. In the original optimization model (Eq. (1)), the value *c*ij represents the added value if employee *i* is allocated to work posi‐ tion *j* and the optimization function maximizes a profit.

$$\max \mathbf{z} = \sum\_{l=1}^{\mu} \sum\_{j=1}^{\mu} \mathbf{c}\_{|j|} \cdot \mathbf{x}\_{|j|} \tag{1}$$

We replaced the added value with the minimal knowledge deficit/surplus (absolute) gap of *n* key required knowledge *Kk* . That means if we allocate an employee with his/her actual knowledge that is nearest to required knowledge on the work position (neither below nor above) then we have attained optimal knowledge alignment. The idea is to minimize the overall absolute key knowledge gap in the processes of the specific com‐ pany (Eq. (2)).

$$\min \mathbf{z} = \sum\_{i=1}^{n} \sum\_{j=1}^{n} \left[ \left( \sum\_{i=1}^{n} \frac{|K\_i|}{n} \right)\_{ij} \cdot \mathbf{x}\_{ij} \right] \tag{2}$$

where *i… n =* number of compared employees; *j… n =* number of different work positions; *k… n =* number of compared key knowledge; and *|Kk | =* absolute difference between required and actual knowledge *K*.

In case of a new required ETO production change, this model can be used in the following situations:


Are these all the possible management actions?

the required level of knowledge has the same result of over‐educated and intelligent employ‐ ees becoming bored when they are executing routine activities [22]. Therefore, we modified a classic assignment linear integer problem of Kolman and Beck [3]. In the original optimization

> *i*=1 *n* ∑ *j*=1 *n*

We replaced the added value with the minimal knowledge deficit/surplus (absolute) gap

actual knowledge that is nearest to required knowledge on the work position (neither below nor above) then we have attained optimal knowledge alignment. The idea is to minimize the overall absolute key knowledge gap in the processes of the specific com‐

> *i*=1 *n* ∑ *j*=1 *n* {(∑ *k*=1 *n* |*K*\_*k*| *n* )*ij*

represents the added value if employee *i* is allocated to work posi‐

. That means if we allocate an employee with his/her

*cij* · *xij* (1)

· *xij*} (2)

model (Eq. (1)), the value *c*ij

of *n* key required knowledge *Kk*

pany (Eq. (2)).

tion *j* and the optimization function maximizes a profit.

*max z* = ∑

**Figure 1.** Knowledge‐based assignment conceptual model.

222 Knowledge Management Strategies and Applications

*min z* = ∑

### **3.2. Measuring the corruption of a perfect process**

As an innovation, the effect of a partial corruption of a perfect process was tested, includ‐ ing its impact on a better knowledge alignment with the limitation that the set of employ‐ ees must remain untouched. The hypothesis was that with a corruption of the process, a better knowledge alignment can be achieved and, consequently, the process efficiency can be increased, despite a simultaneous decrease of its efficiency due to new additional work position breaks. Moreover, there must be a point in the process corruption proce‐ dure after which the inefficiency of the process exceeds the benefits of better knowledge alignment.

The effect of work position breaks in the process is measured by structural index *Kwpb* (Eq. (3)) [27]. This is a common key performance indicator in the theory of analysing business processes.

$$K\_{upb} = \frac{C\_{up}}{P\_s} \cdot 100\tag{3}$$

*Cwp* counts all work position breaks in a specific process. *Pa* counts all activities in that process. In this theory, the process slightly stops each time the next process activity is performed by different employee (on a different work position). This is one of practical causes for additional waiting time in the structure of throughput time of the process. There can be up to *n* − 1 work position breaks in a process of *n* sequential activities. According to the total number of all process activities, a small number of work position breaks means that the process is more efficient.

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 subject of this research.
