**2. Decision-making and decision support systems in PPC**

#### **2.1 Decision-making in operation management**

Already in the 1980s, the *decision-making* has been recognized as a field of central research interest in the area of operation management [8]. Decision-making can be defined "[…]as the process of selecting the course of action that best meets the decision criteria, subject of the constraints inherent in the decision-making situation (DMS)." [9] p. 324.

According to [8] the DMP contains three phases. In the first "intelligence" phase, the problem which requires a solution by the decision-maker is identified and prioritized. Moreover, the first target achievements are defined, and corresponding data gathering is initialized. In the second "design" phase, a general action plan, which contains several action alternatives and their expected outcomes as well as the first evaluation criteria, is defined. In the third "choice" phase, the decisionmaker selects the best action alternative based on the evaluation of each alternative. Based on the early works of [8], several models have been developed in order to explain the DMP. For instance, several authors suggest an extension of [8] DMP model [9, 10]. They propose a fourth "implementation" phase in which the decision outcome is turned into practice. In a fifth "learning" phase, lessons learned are formulated and shared in the organization to improve the DMP and the decision outcome in the future.

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*The Influence of Cognitive Biases in Production Planning and Control: Considering the Human…*

Moreover, also, within PPC human decisions undergo the suggested phases of the DMP models. Typical decision-making situations of PPC are shown in **Figure 1**. PPC contains two subprocesses which are production planning and production control. These two subprocesses contain several task functions which require several decisions. Production planning focuses on the development of the basic concept to determine when to produce what in which quality. Production control has an overall monitoring function to achieve the production targets by the use of

P*roduction program planning* encompasses several decisions about the production sequence and the required materials. Based on this, *quantity planning* determines production quantities and lot sizes. *Due dates and capacity planning* contain several decisions concerning capacity plans and due dates for specific production steps. The *order release* marks the starting point for the production. Since disturbances, such as machine breakdowns, delays in material supply, or quality problems, may occur during production, a continuous *order monitoring* is accomplished. The necessary decisions within these main tasks of PPC are often complex and require the consideration of several parameters. Thus, in PPC typically decision-making is usually supported by DSS. DSSs are often self-developed by using case tools like Crystal,

*DOI: http://dx.doi.org/10.5772/intechopen.89259*

**2.2 Decision-making in production planning and control**

different control techniques.

**Figure 1.**

*PPC model according to [11].*

Analytica, or iThink for the development.

**2.3 Insufficient design guidelines for decision support systems**

While there is a lot of research on DSSs in general (e.g., [12]), as well as on several components and modules of DSS (e.g., [13]), there is only little research about standardized design guidelines for DSS. For instance, [6, 9] criticize the lack of an integrated framework to support a standardized design of DSS. However, [9] propose a framework as the basis for standardized guidelines for DSS designers.

**Figure 2** shows the framework suggested by [9]. The framework contains four levels. (1) The first *decision-making level* is based on the original DMP model of [8] and on its extensions containing all five steps within the decision process. (2) The second *decision service task level* focuses on tasks which require human intelligence and is based on a task-method-subtask structure to infer logical conclusions from the analyzed data. (3) The third *architectural capability level* considers user interface, data information knowledge, and processing capabilities. (4) The fourth *computational symbol-program level*

*The Influence of Cognitive Biases in Production Planning and Control: Considering the Human… DOI: http://dx.doi.org/10.5772/intechopen.89259*

**Figure 1.** *PPC model according to [11].*

*Human 4.0 - From Biology to Cybernetic*

and its outcome [8].

derive first guidelines for DSS.

situation (DMS)." [9] p. 324.

outcome in the future.

economicus." In other words, to apply these models properly, we assume a fully rational human behavior in the decision-making process determined by the purpose of the decision-maker to maximize the personal advantage [4]. Tversky and Kahneman [5] challenged this assumption and showed that human decisions are biased, which means a systematic deviation from rational judgment. In the fields of logistics and PPC, people are often confronted with uncertainty and high complexity, and research has shown that under these framework conditions, humans systematically take wrong decisions [6]. One example for a complex situation in which biased decision-making leads to a deteriorating logistic performance is the so-called lead time syndrome (LTS). Here, production planners overreact to decreasing due date reliability. The planners adapt standard lead times too often, which eventually leads to an even worse aggravation of due date reliability [7]. To support this variety of decisions, which have to be made in PPC, the so-called decision support systems (DSSs) are used frequently. DSSs are computer-based information systems with the purpose to improve the decision-making process

In this chapter we aim to improve the understanding of the role of cognitive biases in the field of PPC and propose first design guidelines for decision support systems (DSSs). Therefore, we combine a systematic literature review on behavioral operation management and cognitive biases. Taking inspiration from a case study from the steel industry, we show the possible impact of cognitive biases on human decision-making in PPC and on logistic performance. The remainder of this chapter is structured as follows. In Section 2, we outline the typical decision-making processes and the corresponding DSS in PPC. In Section 3, we use the case of the PPC at a steel manufacturer to present several examples of the possible impacts of cognitive biases on PPC decision-making. In Section 4, we give first recommendations on how to avoid the emergence of cognitive biases within PPC decision-making and

**2. Decision-making and decision support systems in PPC**

Already in the 1980s, the *decision-making* has been recognized as a field of central research interest in the area of operation management [8]. Decision-making can be defined "[…]as the process of selecting the course of action that best meets the decision criteria, subject of the constraints inherent in the decision-making

the problem which requires a solution by the decision-maker is identified and prioritized. Moreover, the first target achievements are defined, and corresponding data gathering is initialized. In the second "design" phase, a general action plan, which contains several action alternatives and their expected outcomes as well as the first evaluation criteria, is defined. In the third "choice" phase, the decisionmaker selects the best action alternative based on the evaluation of each alternative. Based on the early works of [8], several models have been developed in order to explain the DMP. For instance, several authors suggest an extension of [8] DMP model [9, 10]. They propose a fourth "implementation" phase in which the decision outcome is turned into practice. In a fifth "learning" phase, lessons learned are formulated and shared in the organization to improve the DMP and the decision

According to [8] the DMP contains three phases. In the first "intelligence" phase,

**2.1 Decision-making in operation management**

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