*3.2.3 Observed behavior of planners: cognitive biases underlying deteriorating due date reliability*

We observed several active cognitive biases in various decision-making situations in several PPC tasks. Nevertheless, it is important to understand that this classification of biases is not as concrete in practice as described in theory. Some of the cognitive biases overlap and often occur in several different situations.

#### *3.2.3.1 Memory biases*

Memory biases summarize a group of cognitive biases which are related to the storage and availability of information. The *availability heuristic* describes the tendency of people to overestimate the likelihood of events for which they can easily restore the information [14]. As a result, people tend to overweight the outcome of the last decision as a basis for their decision-making in their current situation. The *imaginability bias* describes the fact that people assume an event to be more probable if it can be easily imagined by themselves [16].

We observe that planners tend to adjust planned lead times based on their intuition instead of entirely considering all influencing variables.

This occurs mainly in the phase of the *production program planning* and *due date and capacity planning*. The planners tend to connect their last updates of the planned lead time to any positive development of the logistic performance. In case of a negative development, the planners assume that external influences such as a delay in material delivery or machine failures are responsible for the fact delayed due date reliability. They conclude that there is a need for another planned lead time adjustment.

We observe the same development for the *production program planning*. In the cases when the defined production sequence leads to a positive performance outcome, the planners relate this development to the quality of their own planning capabilities. In those cases when the defined production sequence leads to a decreasing due date reliability, the planners connected this with external influencing factors and conclude a necessary update on the production sequence, even though this was not optimal for the current situation.

#### *3.2.3.2 Statistical biases*

Statistical biases describe the tendency to over- or underestimate certain statistical parameters. Ref. [14] investigates that humans tend to overestimate the probability of two events occurring together if this has already happened once in

**73**

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

the past. This effect is described by the *correlation bias*. For example, a change in material and an increase in lead times for a certain machine can lead to the assumption that there is a correlation between this material change and extending lead times—which actually does not exist. The *gambler's fallacy* describes the phenomenon of the assumption that future events are determined by the occurrence of past events [17]. This leads to an overestimation of possible events ignoring the actual

We observe statistical biases in the adaption of lead times within the *phase of the order monitoring*. The planners tend to assume that the coincident adaption of planned lead times and the positive development of due date reliability are correlated, although they are aware of the mathematical fact that it takes 4 weeks until the adaptation of planned lead times will become visible in an improved due

We observe these biases also in the *phase of production quantity planning and the order release*. Delays in material provision and machine breakdowns which occurred at the same time lead to the assumption that there is a possible correlation and that this may also increase the system's scrap rate. However, the planners do not further validate this assumption, and the planners simply increase the material orders to reach the desired production output. As a result, the inventory level increases excessively, since the additionally ordered material cannot flow off because the assumed

Confidence biases describe the set of biases concerning the person's confidence in their prowess as a decision-maker. The *illusion of control or overconfidence biases* describes the tendency of people to overestimate their ability to solve difficult problems [19]. The *conformation bias* leads people to seek for information which confirms their own estimation and hides information which is contrary to their own

Analyzing the case study, we find three examples of the *illusion of control* in the *phase of due date planning*. (1) Planners tend to assume that their own procedures are more suitable than the standard planning procedures. (2) When planning the lead times, they behave as if the stable forecast of future incoming orders is predetermined and not only a prediction. (3) Planners increased the WIP level via the planned lead times in order to avoid the production system to run into an idle state. We also found situations exemplary for active confirmation biases. Planners let themselves be guided by their intuition: if planners *feel* that updating the lead times would be the best option to increase due date reliability, they search particularly for information which confirms this feeling. Obvious information which entails the result not to intervene in the planned lead times (such as the given *planning rules* that limit the number of planned lead time updates within a certain period of time)

Confirmation biases were also observed in the phases of *production program planning* and *production quantity planning*. Planners behave as if the estimated future customer demand forecasts are stable and the demand numbers are already fixed. Accordingly, they ordered the corresponding materials and plan production sequences without any buffers accordingly. Moreover, we notice that even when the customer demand is in the course of time and can be determined more specifically (no matter whether it is higher or lower than previous forecasts), planners seek for information which confirms the first numbers in order to justify that they stick to their initial plan (e.g., they search for cases in which certain customers have

increased order quantities at first and then lowered them).

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

statistical possibility [18].

correlation was not true.

*3.2.3.3 Confidence biases*

perception [20].

is ignored.

date reliability.

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

the past. This effect is described by the *correlation bias*. For example, a change in material and an increase in lead times for a certain machine can lead to the assumption that there is a correlation between this material change and extending lead times—which actually does not exist. The *gambler's fallacy* describes the phenomenon of the assumption that future events are determined by the occurrence of past events [17]. This leads to an overestimation of possible events ignoring the actual statistical possibility [18].

We observe statistical biases in the adaption of lead times within the *phase of the order monitoring*. The planners tend to assume that the coincident adaption of planned lead times and the positive development of due date reliability are correlated, although they are aware of the mathematical fact that it takes 4 weeks until the adaptation of planned lead times will become visible in an improved due date reliability.

We observe these biases also in the *phase of production quantity planning and the order release*. Delays in material provision and machine breakdowns which occurred at the same time lead to the assumption that there is a possible correlation and that this may also increase the system's scrap rate. However, the planners do not further validate this assumption, and the planners simply increase the material orders to reach the desired production output. As a result, the inventory level increases excessively, since the additionally ordered material cannot flow off because the assumed correlation was not true.

### *3.2.3.3 Confidence biases*

*Human 4.0 - From Biology to Cybernetic*

*date reliability*

*3.2.3.1 Memory biases*

*3.2.2 Observed behavior of key performance indicators*

The due date reliability was one of the key performance indicators, and 95% was set as a long-term target for the planners. We observed the so-called lead time syndrome active in this context. When planners recognized decreasing delivery reliability, they started to update the initially planned lead times by releasing waiting orders earlier and adding some additional safety lead times in that cases in which the initially lead time was too short to meet the target due date. Thus, more orders are in the production system which causes an increasing WIP level and growing lead times. As a result, the delivery reliability was even lower than before the update of the lead times. The planners feel pressured to improve the current situation, and the circle of updating lead times reinforces, resulting in an even stronger due date aggravation.

**Figure 3** shows the process of the observed planner's behavior.

*3.2.3 Observed behavior of planners: cognitive biases underlying deteriorating due* 

the cognitive biases overlap and often occur in several different situations.

probable if it can be easily imagined by themselves [16].

this was not optimal for the current situation.

intuition instead of entirely considering all influencing variables.

We observed several active cognitive biases in various decision-making situations in several PPC tasks. Nevertheless, it is important to understand that this classification of biases is not as concrete in practice as described in theory. Some of

Memory biases summarize a group of cognitive biases which are related to the storage and availability of information. The *availability heuristic* describes the tendency of people to overestimate the likelihood of events for which they can easily restore the information [14]. As a result, people tend to overweight the outcome of the last decision as a basis for their decision-making in their current situation. The *imaginability bias* describes the fact that people assume an event to be more

We observe that planners tend to adjust planned lead times based on their

This occurs mainly in the phase of the *production program planning* and *due date and capacity planning*. The planners tend to connect their last updates of the planned lead time to any positive development of the logistic performance. In case of a negative development, the planners assume that external influences such as a delay in material delivery or machine failures are responsible for the fact delayed due date reliability. They conclude that there is a need for another planned lead time

We observe the same development for the *production program planning*. In the cases when the defined production sequence leads to a positive performance outcome, the planners relate this development to the quality of their own planning capabilities. In those cases when the defined production sequence leads to a decreasing due date reliability, the planners connected this with external influencing factors and conclude a necessary update on the production sequence, even though

Statistical biases describe the tendency to over- or underestimate certain statistical parameters. Ref. [14] investigates that humans tend to overestimate the probability of two events occurring together if this has already happened once in

**72**

adjustment.

*3.2.3.2 Statistical biases*

Confidence biases describe the set of biases concerning the person's confidence in their prowess as a decision-maker. The *illusion of control or overconfidence biases* describes the tendency of people to overestimate their ability to solve difficult problems [19]. The *conformation bias* leads people to seek for information which confirms their own estimation and hides information which is contrary to their own perception [20].

Analyzing the case study, we find three examples of the *illusion of control* in the *phase of due date planning*. (1) Planners tend to assume that their own procedures are more suitable than the standard planning procedures. (2) When planning the lead times, they behave as if the stable forecast of future incoming orders is predetermined and not only a prediction. (3) Planners increased the WIP level via the planned lead times in order to avoid the production system to run into an idle state. We also found situations exemplary for active confirmation biases. Planners let themselves be guided by their intuition: if planners *feel* that updating the lead times would be the best option to increase due date reliability, they search particularly for information which confirms this feeling. Obvious information which entails the result not to intervene in the planned lead times (such as the given *planning rules* that limit the number of planned lead time updates within a certain period of time) is ignored.

Confirmation biases were also observed in the phases of *production program planning* and *production quantity planning*. Planners behave as if the estimated future customer demand forecasts are stable and the demand numbers are already fixed. Accordingly, they ordered the corresponding materials and plan production sequences without any buffers accordingly. Moreover, we notice that even when the customer demand is in the course of time and can be determined more specifically (no matter whether it is higher or lower than previous forecasts), planners seek for information which confirms the first numbers in order to justify that they stick to their initial plan (e.g., they search for cases in which certain customers have increased order quantities at first and then lowered them).

### *3.2.3.4 Adjustment biases*

Adjustment biases describe the human tendency to stick to the first available information or to a reference point when making decision. The *anchoring effect* is defined as the tendency to rely on an initially given information too heavily—which influences further values [5]. Teng and Das [21] show that anchoring can create systematic errors in decision-making situations. Adjustment biases also include *conservatism bias*. Similar to the anchoring effect, taken estimations are not updated according to new information [22].

This became obvious in the phase of the *order release* and *order monitoring* in the context of lead times. We find that lead times from previous years and from similar work systems act as anchors. When setting planned lead times, planners justify the extension of planned lead times with the numbers in the year 2014. Similarly, the planners tend to aim at a due date reliability of 95%, which is given as the long-term goal (but which is far from reality), and therefore seem to extend the planned lead times disproportionately.

The same effect becomes obvious for the capacity planning. The planners justify their machine capacity planning with target figures of machine utilization rates of the previous years. These figures were not updated to the current situation.

#### *3.2.3.5 Presentation biases*

Presentation biases summarize a set of cognitive biases which influence humans in their decision-making by the way how information is being displayed. The *ambiguity effect* describes the human tendency to favor simple-looking options and avoid options that seem to be complicated [23]. According to the *primacy/recency effect,* information at the beginning and at the end of a series can be restored best, whereas information in the middle are restored worst [24].

The implemented DSS offers plenty of types of analysis (such as the order forecasts, inventory levels, etc.) next to the information which is central for setting planned lead times. The *primacy recency effect* became obvious in the phase of order monitoring when the planner was setting the planned lead time for a certain order to the double value of what was reasonable. This is because he had just checked the current due date reliability and noticed that the value for the previous day was particularly low.

Further, we identified the influence of the ambiguity effect in the material quantity planning. When planners recognized that the production could run out of material, they just increased the initially ordered quantity. They did not further analyze potential causes like an increasing scarp rate due to an incorrectly set machine, etc. Instead they took the simplest option right in front of them to keep the production running even though the failure cause exponentiated.

#### *3.2.3.6 Situation biases*

Situation biases describe the way how a person responds to the general decision situation. The *complexity effect* describes that people are biased under time pressure or when information overload occurs [25]. The *ostrich effect* describes the habit of people to ignore an obvious negative information [26]. The *bandwagon effect* describes the tendency to do things because many other people do the same [4].

We identify situation biases in several tasks in PPC. Modern PPC DSSs provide a wide range of information, such as key performance indicators concerning delivery reliability, inventory levels, or throughput times. For many planners, the amount and variety of information are too much to be included in their

**75**

**Figure 4.**

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

decision-making. In particular, under time pressure the planners decide to extend lead times just to do anything, even when they do not come to a reasonable conclusion when analyzing the data. At the same time, the planners ignore the fact that their own behavior of extending lead times influences due date reliability in a negative way. Moreover, we find that adjusting lead times is a common method of reacting to decreasing due date reliability. Planners who face the situation of decreasing due date reliability choose planned lead time extension just because their colleagues do so. Also, in the case of a machine breakdown, a similar behavior could be observed. The closer the delivery due date, the more planners decided to switch machines and change the production program sequence just to do anything. This was even true when the effort and time to change machines take in total

**Figure 4** shows a summary of our observed cognitive bias categories within the several PPC tasks. Potentially, there are even more active biases in the several PPC

DSSs intend to improve the decision outcome by supporting the human decision-making process [6]. Therefore, in the design of DSSs, also human behavioral aspects need to be considered to get an unbiased decision outcome. Based on our identified cognitive biases in PPC decisions, we aim to give first recommendations

The proposed framework of [9] serves as the basis and is extended by a socalled behavioral layer. In this, already in the design phase, the DSS should foresee adequate *debiasing techniques* to support planners properly and thus to positively

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

longer than the repair of the initial machine.

*Observed cognitive biases in the case of steel production PPC.*

tasks, which we did not observe in our case.

for system developers of DSS.

**4. Debiasing by the design of decision support systems**

affect logistic performance of the production system.

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


**Figure 4.**

*Human 4.0 - From Biology to Cybernetic*

according to new information [22].

times disproportionately.

*3.2.3.5 Presentation biases*

particularly low.

*3.2.3.6 Situation biases*

information in the middle are restored worst [24].

Adjustment biases describe the human tendency to stick to the first available information or to a reference point when making decision. The *anchoring effect* is defined as the tendency to rely on an initially given information too heavily—which influences further values [5]. Teng and Das [21] show that anchoring can create systematic errors in decision-making situations. Adjustment biases also include *conservatism bias*. Similar to the anchoring effect, taken estimations are not updated

This became obvious in the phase of the *order release* and *order monitoring* in the context of lead times. We find that lead times from previous years and from similar work systems act as anchors. When setting planned lead times, planners justify the extension of planned lead times with the numbers in the year 2014. Similarly, the planners tend to aim at a due date reliability of 95%, which is given as the long-term goal (but which is far from reality), and therefore seem to extend the planned lead

The same effect becomes obvious for the capacity planning. The planners justify their machine capacity planning with target figures of machine utilization rates of

Presentation biases summarize a set of cognitive biases which influence humans in their decision-making by the way how information is being displayed. The *ambiguity effect* describes the human tendency to favor simple-looking options and avoid options that seem to be complicated [23]. According to the *primacy/recency effect,* information at the beginning and at the end of a series can be restored best, whereas

The implemented DSS offers plenty of types of analysis (such as the order forecasts, inventory levels, etc.) next to the information which is central for setting planned lead times. The *primacy recency effect* became obvious in the phase of order monitoring when the planner was setting the planned lead time for a certain order to the double value of what was reasonable. This is because he had just checked the current due date reliability and noticed that the value for the previous day was

Further, we identified the influence of the ambiguity effect in the material quantity planning. When planners recognized that the production could run out of material, they just increased the initially ordered quantity. They did not further analyze potential causes like an increasing scarp rate due to an incorrectly set machine, etc. Instead they took the simplest option right in front of them to keep

Situation biases describe the way how a person responds to the general decision situation. The *complexity effect* describes that people are biased under time pressure or when information overload occurs [25]. The *ostrich effect* describes the habit of people to ignore an obvious negative information [26]. The *bandwagon effect* describes the tendency to do things because many other people do the same [4]. We identify situation biases in several tasks in PPC. Modern PPC DSSs provide

a wide range of information, such as key performance indicators concerning delivery reliability, inventory levels, or throughput times. For many planners, the amount and variety of information are too much to be included in their

the production running even though the failure cause exponentiated.

the previous years. These figures were not updated to the current situation.

*3.2.3.4 Adjustment biases*

**74**

*Observed cognitive biases in the case of steel production PPC.*

decision-making. In particular, under time pressure the planners decide to extend lead times just to do anything, even when they do not come to a reasonable conclusion when analyzing the data. At the same time, the planners ignore the fact that their own behavior of extending lead times influences due date reliability in a negative way. Moreover, we find that adjusting lead times is a common method of reacting to decreasing due date reliability. Planners who face the situation of decreasing due date reliability choose planned lead time extension just because their colleagues do so. Also, in the case of a machine breakdown, a similar behavior could be observed. The closer the delivery due date, the more planners decided to switch machines and change the production program sequence just to do anything. This was even true when the effort and time to change machines take in total longer than the repair of the initial machine.

**Figure 4** shows a summary of our observed cognitive bias categories within the several PPC tasks. Potentially, there are even more active biases in the several PPC tasks, which we did not observe in our case.
