1.Decomposing/restructuring


We used these categories as a basis for the development of the first design guidelines for DSS in the field of PPC.

**(1) Decomposing/restructuring**: By applying this method, the decision and the related information are restructured and separated to match the task and the capabilities of the decision-maker [29]. Therefore, decisions, such as production program planning within PPC, should be split into single tasks. In other words, the production program planning should be broken down into the subtasks of the decision about the production of several product categories, the corresponding quantities, and the due dates. In order to avoid an information overload for the DSS user and the occurrence of the situation biases (which may cause losing the overview of the connection between single parameters), the DSS should only show the most relevant information for a decision. Additional parameters should be available in the system in the background and should be provided upon request. For example, a machine breakdown can entail a decision update on the capacity planning because the production quantity originally planned on the broken machine has to be switched to another machine. Occasionally, this can also result in a necessary update of the production program. If this is the case, only in the moment when a decision becomes relevant, the request should be provided.

**(2) Put yourself in the shoes of:** The objective of this method is to enable the decision-maker to consider all the influencing parameters of affected parties through a perspective shift [28]. To enable this method, a pre-analysis of the affected stakeholders of potential decision cases is required.

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achieve this goal.

framework presented by [9].

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

Within the DSS this method can be applied in two ways. (1) First, before making a final decision, potential scenarios can be presented to the decision-maker. This should also include the implications for logistic performance (e.g., inventory levels, lead time, due date reliability). For example, the implications of a change the production program may have for machine and personnel capacity as well as for material requirement planning should be made visible to the planner even if these implications are only of interest for other departments (e.g., the logistics and the purchasing department). (2) Second, historical data on the decision-making and the outcomes in terms of logistic performance can be provided (e.g., the decision about the lead time in the previous month caused this delivery reliability).

**(3) Draw attention to alternative outcome:** This method focuses on alternative outcomes to avoid the confirmation biases seeking for supportive information on the initial hypotheses. Thinking about counter explanations as well as the opposite intention and perspective can broaden one's own decision-making horizon. For example, in the case of a machine failure, the first intention of planners in our observed case was to switch the machines to stick to the production due date. However, this caused additional setup time. The opposite intention here would be to stick to the initial planned machine and wait for the machine to be repaired or start with some tasks which can be done without the machine to avoid losing time due to the machine breakdown and avoiding additional setup time at the same time. **(4) Devil's advocate:** This debiasing strategy focuses on the possible critique of other parties affected by the taken decision. Thereby, the *devil's advocate* argues against the position of the decision-maker. Through this presentation of a formalized dissent, the decision-maker is forced to proof his decision and find supportive arguments. Research has shown that this leads to better solutions [28, 30]. An important criterion to apply this method successfully is that the devil's advocate is nonemotional in raising his dissent [31]. Therefore, including this method in a DSS is appropriate to fulfill this criterion. Before executing the final decision regarding the extension of a planned lead time, a pop-up window should arise and present a summary of all possible negative effects linked to the question whether the decision-maker is sure about continuing with his decision. Exemplarily, in a case of intended machine switch which also causes setup requirements for tooling, etc., the

**(5) General bias awareness:** The general awareness of the existence of cognitive biases can be understood as an overall debiasing strategy. Even if the general understanding of the underlying influencing factors on decisions can improve decisional judgment quality, it cannot completely eliminate its emergence [32]. A wider understanding of the influence of cognitive biases on decision-making can be achieved, for example, by provision of short training videos or a user tutorial explaining the influence of cognitive biases. This can be the starting point before using the DSS tool initially. The *general bias awareness* can also be affected by the layout of the graphical user interface as well as by the structure of the DSS which should be well organized and intuitively understandable. This contributes to the avoidance of *situation biases* due to an information overload. Moreover, in attention to the *statistical and the anchoring biases*, just presenting simple figures should be avoided, and additional context information should be added. Based on our observed case study of the long-term target delivery reliability of 95% which acted as an anchor and was quite unrealistic, it would be better to give additional information such as a delivery reliability target for each month and more content information about corresponding developments such as an allowed inventory level or machine utilization rate to

**Figure 5** shows our proposition for a further design layer for the DSS design

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

system should ask whether this really should be done.

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

Within the DSS this method can be applied in two ways. (1) First, before making a final decision, potential scenarios can be presented to the decision-maker. This should also include the implications for logistic performance (e.g., inventory levels, lead time, due date reliability). For example, the implications of a change the production program may have for machine and personnel capacity as well as for material requirement planning should be made visible to the planner even if these implications are only of interest for other departments (e.g., the logistics and the purchasing department). (2) Second, historical data on the decision-making and the outcomes in terms of logistic performance can be provided (e.g., the decision about the lead time in the previous month caused this delivery reliability).

**(3) Draw attention to alternative outcome:** This method focuses on alternative outcomes to avoid the confirmation biases seeking for supportive information on the initial hypotheses. Thinking about counter explanations as well as the opposite intention and perspective can broaden one's own decision-making horizon. For example, in the case of a machine failure, the first intention of planners in our observed case was to switch the machines to stick to the production due date. However, this caused additional setup time. The opposite intention here would be to stick to the initial planned machine and wait for the machine to be repaired or start with some tasks which can be done without the machine to avoid losing time due to the machine breakdown and avoiding additional setup time at the same time.

**(4) Devil's advocate:** This debiasing strategy focuses on the possible critique of other parties affected by the taken decision. Thereby, the *devil's advocate* argues against the position of the decision-maker. Through this presentation of a formalized dissent, the decision-maker is forced to proof his decision and find supportive arguments. Research has shown that this leads to better solutions [28, 30]. An important criterion to apply this method successfully is that the devil's advocate is nonemotional in raising his dissent [31]. Therefore, including this method in a DSS is appropriate to fulfill this criterion. Before executing the final decision regarding the extension of a planned lead time, a pop-up window should arise and present a summary of all possible negative effects linked to the question whether the decision-maker is sure about continuing with his decision. Exemplarily, in a case of intended machine switch which also causes setup requirements for tooling, etc., the system should ask whether this really should be done.

**(5) General bias awareness:** The general awareness of the existence of cognitive biases can be understood as an overall debiasing strategy. Even if the general understanding of the underlying influencing factors on decisions can improve decisional judgment quality, it cannot completely eliminate its emergence [32]. A wider understanding of the influence of cognitive biases on decision-making can be achieved, for example, by provision of short training videos or a user tutorial explaining the influence of cognitive biases. This can be the starting point before using the DSS tool initially. The *general bias awareness* can also be affected by the layout of the graphical user interface as well as by the structure of the DSS which should be well organized and intuitively understandable. This contributes to the avoidance of *situation biases* due to an information overload. Moreover, in attention to the *statistical and the anchoring biases*, just presenting simple figures should be avoided, and additional context information should be added. Based on our observed case study of the long-term target delivery reliability of 95% which acted as an anchor and was quite unrealistic, it would be better to give additional information such as a delivery reliability target for each month and more content information about corresponding developments such as an allowed inventory level or machine utilization rate to achieve this goal.

**Figure 5** shows our proposition for a further design layer for the DSS design framework presented by [9].

*Human 4.0 - From Biology to Cybernetic*

lying influencing factors

and observed over time.

supplier selection processes:

4.Devil's advocate

5.General bias awareness

guidelines for DSS in the field of PPC.

1.Decomposing/restructuring

2.Put yourself in the shoes of

3.Draw attention to alternative outcome

tive debiasing:

Debiasing is a method to reduce or eliminate the influence of cognitive biases within the decision process. Keren [27] proposed the following three steps for effec-

1.Identification of the existence and nature of the potential bias and the under-

3.Monitoring and evaluation of the effectiveness of the selected debiasing technique

The proposed steps should be included in the design of a DSS. Based on our findings about the active cognitive biases in PPC, we already fulfilled the first step. In this section we contribute to the second step and aim to propose ways and techniques to lower the impact of biases. The third step then needs to be analyzed

These steps form the generic basis for a debiasing approach which contains

We used these categories as a basis for the development of the first design

the production quantity originally planned on the broken machine has to be

becomes relevant, the request should be provided.

affected stakeholders of potential decision cases is required.

switched to another machine. Occasionally, this can also result in a necessary update of the production program. If this is the case, only in the moment when a decision

**(2) Put yourself in the shoes of:** The objective of this method is to enable the decision-maker to consider all the influencing parameters of affected parties through a perspective shift [28]. To enable this method, a pre-analysis of the

**(1) Decomposing/restructuring**: By applying this method, the decision and the related information are restructured and separated to match the task and the capabilities of the decision-maker [29]. Therefore, decisions, such as production program planning within PPC, should be split into single tasks. In other words, the production program planning should be broken down into the subtasks of the decision about the production of several product categories, the corresponding quantities, and the due dates. In order to avoid an information overload for the DSS user and the occurrence of the situation biases (which may cause losing the overview of the connection between single parameters), the DSS should only show the most relevant information for a decision. Additional parameters should be available in the system in the background and should be provided upon request. For example, a machine breakdown can entail a decision update on the capacity planning because

Kaufmann et al. [28] propose five categories for effective debiasing strategies in

further debiasing categories describing the concrete method of debiasing.

2.Consideration of ways and techniques to lower the impact of bias

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**Figure 5.**

*DSS design framework presented by [9] with our proposition of an additional layer for the DSS design framework.*
