Understanding decision making in supply chain resilience: The influence of SCRES strategies on decision making

Understanding decision making in supply chain resilience:

The influence of SCRES strategies on decision making

Master Thesis Supply Chain Management, MSc

University of Groningen, Faculty of Economics and Business

Tony Mullié

Student number: s2217252

E-mail: a.r.mullie@student.rug.nl

Word count: 11863

Supervisor, University of Groningen: Dr. K. Scholten

Co-Assessor, University of Groningen: X. Tong

January 29th, 2018

Acknowledgements:

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Abstract

Purpose – This study aims to understand how decisions are being made in response to a supply chain disruption, and how different supply chain resilience strategies influence these decisions. The objective is to explain how different resilience configurations lead to different ways of decision making.

Methodology / Design – Exploratory interviews were chosen to investigate the how decisions are being made. These interviews followed the critical incident approach to grasp the individuals perspective while taking into account cognitive and behavioural elements. It was conducted in multiple industries with 7 interviews providing examples of 14 decision making processes.

Findings – The use of both rationality and intuition were identified in the decision making process dependent on different supply chain resilience properties. The combination of rationality and intuition was found to lead to the most effective decision making. Mechanisms such as the availability of options, information and communication were identified to improve this process.

Implications / Value – This study is one of the first to investigate the decision making based on supply chain resilience aspects in detail. It is the first study to incorporate the concept of intuition in supply chain resilience, following up supply chain in general and supply chain supplier selection decisions. The development of the decisions making model provides insights for both theory and practice.

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Table of contents

1. Introduction

……….………3

2. Theoretical Framework…………

………5

2.1 Supply Chain Resilience………….………5

2.1.1 Flexibility………....…………...………...5

2.1.2 Collaboration……….………..6

2.1.3 Velocity.…….………...6

2.1.4 Visibility……….…………..………..………...7

2.2 Decision Making………8

2.2.1 Rational Decision Making……….………..9

2.2.2 Intuition…………...……….10 2.3 Conceptual Model..…..………12

3. Methodology

……….13 3.1 Research Setting……….………..13 3.2 Data Collection……….………14 3.3 Coding Procedures…...……...……….………16 3.4 Data analysis……….………...……….………18

4. Findings

....….………...……….19

4.1 Findings regarding flexibility………..………..21

4.2 Findings regarding collaboration…………..…….………22

4.3 Findings regarding velocity.………..………24

4.4 Findings regarding visibility………..………25

4.5 Findings regarding the outcome of decisions...………..………27

5. Discussion

….………...……….29

5.1 Decision making in SCRES……….………..29

5.2 Decision making and SCRES strategies....………31

5.3 Decision making and perceived outcome..………33

6. Conclusion

....………...……….33

6.1 Managerial implications………..………..34

6.2 Limitations and future research….……....………34

References

……….………...……….36

Appendix A

…..……….………...……….42

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1. Introduction

In today’s highly complex and increasing competitive business environment, supply chains are more vulnerable to disruptions that lead to for instance lost sales and stock outs (Wu, Blackhurst, & O’Grady, 2007). As such, building a resilient supply chain and making the right decisions is vital for those affected companies in order to survive. Supply chain resilience (SCRES) is the ability of a supply chain to return to its normal operating performance within an acceptable period of time, after it has been disturbed (Christopher & Peck, 2004) by for instance natural disasters like lightning,

hurricanes, earthquakes, volcanos and tsunamis or part shortages, quality issues and communication issues (Blackhurst, Craighead, Elkins, & Handfield, 2005; Wu et al., 2007). The speed and success of the supply chain’s response and recovery largely depends on the choices its supply chain manager makes (Macdonald & Corsi, 2013). Managers facing these kind of disruptions have to make decisions in an environment where there is a lot of stress, limited information and a limited amount of time. With an estimate of $50 to $100 million cost a day when a disruption occurs (Rice Jr & Caniato, 2003), the pressure on these decisions is huge. Yet, research to date gives very little insights in how these decisions are made, and how these can be improved.

Current supply chain resilience literature focusses on a lot of different strategies on how supply chain resilience can be improved, as for instance flexibility, collaboration, velocity and visibility (Jüttner & Maklan, 2011; Pettit, Fiksel, & Croxton, 2010; Scholten & Schilder, 2015; Sheffi & Rice Jr., 2005). These strategies allow the supply chain to respond and recover as fast as possible, after a disruption has happened (Christopher & Peck, 2004). However, very few researchers specifically investigated the decision making while responding to a supply chain disruption. Bode and Macdonald (2016) did look at these decisions, and approached the decision making from a rational perspective. They state that managers first recognize the problem, diagnose and gather information, analyse it and develop a response, and finally implement the chosen response. This analytical approach in making decisions is said to lead to the best decision outcome (Slotegraaf & Atuahene-Gima, 2011).

4 2008), recent supply chain literature in the field of buyer selection decisions start to incorporate the concept of intuition and have proven the use of intuition (Carter, Kaufmann, & Wagner, 2017; Kaufmann, Meschnig, & Reimann, 2014). They have shown that under conditions of time pressure and uncertainty, intuition might lead to better decision outcomes (Sadler-Smith, 2016). Besides, although SCRES strategies are proven to enhance supply chain resilience (Jüttner & Maklan, 2011), no research to date has investigated how these strategies influence the decision making when

responding to a supply chain disruption. Therefore, since there seems to be an inconsistent view of the decision making process and little understanding about how SCRES strategies play a role within those decisions, this paper addresses this gap by answering the following research question:

How are decisions being made due to supply chain resilience strategies in response to supply chain disruptions and what is their outcome?

The research is qualitative (exploratory interviews) in nature and will be conducted using semi-structured interviews at different supply chain firms. It aims at contributing to existing literature by specifically investigating decision making in response to a supply chain disruption and whether they use a rational, intuitive or a combination approach to decision making. This research will be the first to incorporate and consider the concept of intuition in supply chain resilience. Besides, it also allows improved understanding on how the different SCRES strategies influence decision making when responding to a supply chain disruption.. The insights obtained during this research enables organizations to really understand the decision making process during SCRES and how SCRES strategies can help their decision makers when faced with uncertainty and time pressure.

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2. Theoretical framework

2.1 Supply chain resilience

Supply chain disruptions can be considered as unplanned and unanticipated events that disturb the normal flow of goods, materials and services (Craighead, Blackhurst, Rungtusanatham, & Handfield, 2007). These disruptions can be costly and eventually lead to stock-outs and the inability to meet customer demand (Blackhurst et al., 2005). The concept of supply chain resilience aims at returning the supply chain to its normal operating performance after such a disruption happened (Christopher & Peck, 2004). Regarding this research, supply chain resilience is defined as “the adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function” (Ponomarov & Holcomb, 2009: 131). The adaptive capability means that the supply chain can respond differently to the different threats it faces (Tukamuhabwa, Stevenson, Busby, & Zorzini, 2015). What becomes clear from this definition is that supply chain resilience consists of a proactive and reactive part. The proactive part contains strategies to prepare for the unexpected events (e.g. disruptions), which is before the disruptions happens. The reactive part contains strategies after the unexpected event (e.g. disruption) happened, during the response and recovery of the supply chain. Literature has many different supply chain resilience strategies which can be used to respond to a supply chain disruption (Tukamuhabwa et al., 2015). However, the most common and used strategies in literature which help to respond to a disruption are flexibility, collaboration, velocity and visibility (Jüttner & Maklan, 2011; Pettit, Fiksel, & Croxton, 2010; Scholten & Schilder, 2015; Sheffi & Rice, 2005). Therefore this study will elaborate on these most used strategies from the perspective of responding to a disruption.

2.1.1 Flexibility

6 flexibility provides the availability of alternative choices such as different suppliers (Sheffi & Rice, 2005). For example, when a manufacturer faces a disruption (e.g. a supplier’s production line breaks down), flexibility would allow the company to produce the product elsewhere (Swamidass & Newell, 1987). Having this flexibility comes forward from risk mitigation strategies (Tang & Tomlin, 2008) and can express itself in having back-up facilities to maintain the production process (Rezapour, Farahani, & Pourakbar, 2017). Therefore, flexibility enables the organization to respond and recover quickly after a disruption occurred.

2.1.2 Collaboration

Collaboration provides the opportunity for organizations to exchange and share knowledge and resources to recover from a disruption (Scholten et al., 2014; Jüttner & Maklan, 2011). It refers to the ability to work with other entities to obtain a mutual benefit (Pettit et al., 2013). The underlying principle is that working together enables the exchange of information and therefore reduces uncertainty (Christopher & Peck, 2004). For instance, in the automotive industry brands like Toyota, Jaguar and Aston Martin collaborate to share information and create visibility of inventory data to benefit the entire industry (Brandon-Jones, Squire, Autry, & Petersen, 2014). The exchange of information between different entities will eventually create a greater visibility throughout the entire supply chain (Christopher & Peck, 2004). Also, collaboration will lead to reduced lead times and better flexibility, which makes collaboration also an enabler of visibility and flexibility (Scholten & Schilder, 2015).

2.1.3 Velocity

7 found in their study that the lower the total response speed, the lower will be the impact of a supply chain disruption and therefore increases supply chain resilience. Regarding the recovery of the supply chain, most researchers approach the recovery phase from a velocity perspective in which recovery is measured in terms of recovery time (Christopher & Peck, 2004; Sheffi & Rice, 2005). Therefore, velocity is crucial during the response and recovery (Jüttner & Maklan, 2011).

2.1.4 Visibility

Visibility is referred to the capability of “being perceived by the eye or mind” (Jüttner & Maklan, 2011: 248). It allows to see through the entire supply chain (Christopher & Peck, 2004). Seeing through the entire supply chain prevents for over-reactions, unnecessary interventions and ineffective decisions (Christopher & Lee, 2004). In other words, visibility reduces the negative impacts of a supply chain disruption. Macdonald and Corsi (2013) state that the initial decision, whether or not to communicate a potential disruption based on incoming information, is also an important factor. Visibility provides the opportunity to make a better initial decision by allowing to see through the entire supply chain (Christopher & Lee, 2004). Visibility is therefore more than just information sharing, it is also about inventory and demand levels to make the entire supply chain more transparent (Brandon-Jones et al., 2014).

The previously mentioned strategies and their corresponding definitions are summarized in table 1.

Table 1 Supply chain resilience strategies

Strategy Definition

Flexibility The ability to adapt to both positive and negative influences of the environment (Ponomarov & Holcomb, 2009)

Collaboration The ability to work together with other entities while creating a mutual benefit (e.g. information and resource sharing) (Tukamuhabwa et al., 2015)

8 Visibility The ability to see through the entire supply chain to be able to effectively

respond and recover from a disruption (Tukamuhabwa et al., 2015)

While current literature elaborates on all the different strategies that help in preparing, responding and recovering from a supply chain disruption (Tukamuhabwa et al., 2015), it seems that the role of the decision maker who ultimately has to decide upon these strategies, has been overlooked. This is particularly important during the reactive part of supply chain resilience, since the speed and success in which the organization recovers largely depends on the choices its supply chain managers makes (Macdonald & Corsi, 2013). Although some authors mention the decision maker and some external precaution actions such as the number of decision makers and the importance of the initial decision (Macdonald & Corsi, 2013), making these decisions is not as straightforward as most models suggest. Decision making of the managers is constrained by time and cognitive limitations (Pearson & Clair, 1998). Moreover, supply chain managers need to make decisions in an uncertain and time-constrained environment. Due to these circumstances, data might not be available or is often incomplete, which makes it difficult to identify, diagnose and respond to supply chain disruptions (Azadegan, Patel, Zangoueinezhad, & Linderman, 2013). The design of the supply chain could make this data more or less available, and might lead to different ways of decision making. Although the environmental pressure plays a huge role in these decisions, no one actually looked at these specific decisions and how these strategies actually might help the decision maker.

2.2 Decision Making

9 time and uncertainty play a role, just as when the supply chain responds to a disruption, decision makers use some kind of intuition (Klein, Calderwood, & Clinton-Cirocco, 1986). This approach is called dual process theory, which implies that a human has two kinds of information processing systems (Evans, 2007). Many researchers gave different names to these two processing modes, such as intuitive and analytic, experimental and rational, tacit and deliberate, automatic and controlled, or holistic and analytic, decision making (Hogarth, 2010; Stanovich & West, 2000). In practice, decision making will probably contain elements of both types of decision making to have the most effective decision (Kaufmann et al., 2014; Mishra et al., 2015). This research will use the concepts of intuition and rational decision making.

2.2.1 Rational decision making

10 supply chain should therefore help making a decision while using procedural rationality, since it allows to see through the supply chain and provide relevant information. Having limited information does not enhance rational decision making, since it relies upon analysing information thoroughly (Haines, Hough, & Haines, 2010). Therefore, it makes it likely that a decision maker does not solely rely on rational decision making after a supply chain disruption happened (Knemeyer & Naylor, 2011).

2.2.2 Intuition

Intuition has become of more interest to the supply chain management literature (Carter et al., 2017). It is not the opposite of rationality or some random guessing (Khatri & Ng, 2000), it allows take a more holistic perspective in complex situations in which time pressure and uncertainty play an important role (Khatri & Ng, 2000; Sadler-Smith, 2016). If a supply chain should react as fast as possible, which means the velocity is high, this could lead to a more intuitive decision. Intuitive decision making is associated with positive outcomes in these complex situations where information uncertainty plays a large role (Dane & Pratt, 2007). Although there exists many definitions of intuition, this research defines it as “a nonconscious, holistic processing mode in which judgments are made with no awareness of rules of knowledge use for inference and can feel right despite one’s ability to articulate the reason” (Shapiro & Spence, 1997: 64). Intuitive decision making is associated with positive outcomes in changing environments where there is little or no tangible information (Dane & Pratt, 2007), which therefore might come forward from responding to a supply chain disruption. Intuition is mostly referred to as ‘gut feel’, ‘hunch’ or ‘vibes’ and is the outcome of non-conscious and affective processes which result in rapid holistic evaluations (Dane & Pratt, 2007). It contains emotional and experience inputs which makes the decision making fast and require less of the cognitive capacity (Hodgkinson, Sadler-Smith, Burke, Claxton, & Sparrow, 2009). Carter et al. (2017) performed a study in which they conceptualized intuition in a supply chain management context. They state that intuition consists of three dimensions: experience-based processing, emotional processing and automatic processing. These will be used in this research and are elaborated in the next section.

11 This type of decision making comes from Klein, Calderwood, and Clinton-Cirocco (1986), who developed the recognition primed decision (RPD) model which describes how experts use their experience in order to make decisions. This model was developed in operational settings and explains how people make extremely rapid decisions without comparing all different options (Klein, 2008). This model suits a complex situation in which limited time and ambiguous information play an important role (Mishra et al., 2015) and it shows that proficient decision makers perform (exceptionally) well during these circumstances using for instance pattern matching (Lipshitz et al., 2001). When a certain situation becomes familiar, the analysis will be subconscious and rapid (Miller & Ireland, 2005). For example, when a supply chain manager faces a disruption in which a supplier cannot deliver, but he experienced this disruption a year ago as well, he will not analyse the entire situation but makes a decision based on his previous experience. Therefore, a repetitive event might also lead to an increase of experience-based processing.

Emotional processing

Emotional processing concerns the ‘gut feel’, ‘hunch’ or ‘vibes’ which decision makers feel when they make a decision (Dane & Pratt, 2007). This feeling mostly has an unconscious effect on the decision maker, in which he does not know it affects his decision (Sadler-Smith & Shefy, 2004a). A positive mood has been linked to an increased use of intuition and a decrease in the rational approach (see Weiss & Cropanzano, 1996). For example, when a supply chain manager has to choose a new supplier to replace the missing parts due to a disruption, he might choose one over another because he has a positive feeling about them. Collaboration might therefore affect certain decisions of the managers, depending on whether he has a good or bad feeling about them.

Automatic processing

12 velocity plays an important part during the reactive part of supply chain resilience, it is likely that decision makers will use more automatic processing to make quick decisions. It will probably lead to decisions in which the decision maker did not gave it much thought, but just knew what to do.

2.3 Conceptual Model

13 Figure 2.1 Conceptual Model

3. Methodology

This research aims to gain insight into how supply chain managers make decisions in supply chain resilience, specifically in responding to supply chain disruptions. Since the concept of decision making is relatively new in supply chain resilience, the purpose is mainly exploratory. Therefore, a qualitative approach is used. This research conducted an exploratory interview approach since it allows investigators to understand the subjective feelings and reactions in a disturbing situation (Crouch & McKenzie, 2006). Doing interviews might produce richness of material if the researcher is responsive to cues as they occur in the course of the interview (Crouch & McKenzie, 2006). Information obtained through interviews provided the necessary understanding to go in depth and analyse the decision making process. Accordingly, the unit of analysis, derived from the research question, is the decision making process coming forward from supply chain resilience strategies.

3.1 Research setting

14 complexity of the processes involved and made them more vulnerable. But also, this increased the probability of having a disruption in which they need to collaborate with another company. Moreover, if more companies feel the consequences on one disruption, velocity plays even a larger role and it increased the pressure on the decision makers.

3.2 Data Collection

To gain insight into how supply chain managers make their decisions, ten supply chain (or related) managers who work within the previous described industries setting were selected. Selection was based on the information on the company’s website, LinkedIn, or personal experience with the company or supply chain manager. As the main interest of this study is responding to supply chain disruptions, the supply chain managers were selected on the premise that they actually contributed to this part and made final decisions. Given the aspect of experience-based processing in intuition, managers should have at least 5 years of experience to be able to rely on it. The chosen managers had at least 10 years working experience and were diverse in the amount of encountered disruptions, stretching from a couple to far over 20. Table 3.1 provides an overview of all managers who participated in this research. It shows that all managers had working experience and worked in different kind of industries.

Table 3.1 Overview of data collection

Age Company industry Function # Years of working experience # Years of experience in current function # Encountered supply chain disruptions # Length of interview Manager 1 40 - 50 Electronics, Healthcare, Lightning Procurement manager 21 - 30 3 - 6 6 - 10 60 min

Manager 2 41 - 50 Chemical Department manager

11 - 20 3 - 6 20 + 45 min

Manager 3 41 – 50 Chemical Procurement manager 21 - 30 7 - 9 20 + 45 min Manager 4 31 - 40 Electronics, Healthcare, Lightning Supply chain manager 11 - 20 0 - 3 20 + 75 min

Manager 5 51 - 60 Hygiene Procurement manager

15 Manager 6 51 – 60 Chemical Supply chain

Manager 21 - 30 7 – 9 6 - 10 45 min Manager 7 51 - 60 Wooden package solutions Department manager 21 - 30 3 - 6 20 + 60 min

Before each interview, potential interviewees were briefed about the purpose of the study and the topics to be discussed by means of an interview protocol and interview guide. A semi-structured interview was used to gather the data for this research, which can be found in Appendix A. The first part of the interview was concerned with some closed-ended questions such as the participant’s age, years of experience and approximate number of previous encountered disruptions. The second part contained open-ended questions, in which the participant described things such as information about the company, the disruption, and the decisions he made to recover from the disruption. The questions regarding the decision making were developed using the critical incident technique (CIT) (Flanagan, 1954). In essence, the technique asks open-ended questions, and want the participants to do most of the talking and describe how they responded to a particular accident, or in this case, a disruption. In contrast to closed-ended questions, it gives the opportunity to grasp the individuals perspective while taking into account cognitive and behavioural elements (Coetzer, Redmond, & Sharafizad, 2012). In this case, as can be seen in Appendix A, the participant was first asked to describe the disruption in detail, followed by what decisions he made during this disruption. After he described these decisions, he was asked why he made them to be able to reflect on those decisions. Follow up questions were asked if a participant was unclear in his answer why he made a certain decision. A limitation of this technique is that one might not recall all details of a past disruption (Butterfield, Borgen, Amundson, & Maglio, 2005). Therefore, the participants were asked to think about two disruptions upfront which happened in the past two years.

16 interviewees for review and to be approved., which ensured construct-validity (Ellram, 1996). Each interview took between 45 and 75 minutes and was conducted on the premises of the manager’s company. Having a case study protocol with corresponding interview guide and procedures to be followed ensured the reliability of this study (Ellram, 1996).

3.3 Coding Procedures

Before the coding procedure started, each interview was transcribed. After the first coding procedures, the data was reduced by removing all overlapping descriptions which led to 133 quotes in total. Then all quotes, which are relevant in answering the research question, contained sentences or phrases to know the context in which the quotes were said. This leads to less incorrect coding since one word might not grasp the entire meaning of the interviewee’s answer. Second, the data was coded deductively for decision making based on the concepts of rationality and intuition. The procedures were the same as in which Carter et al. (2017) used labels to find the three major dimensions of intuition which are described in the theoretical framework. This means that experience-based processing included labels such as “knowledge,” “experience,” “memory,” “recognition,” and “parallels”. Emotional processing contains labels such as “gut-based”, “gut-feeling”, “stomach”, “hunch”, “excitement”, “affective” and “feeling”. Finally, automatic processing includes labels such as “immediate”, “automatic”, “quickly”, “spontaneous”, “without much awareness” and “direct”. Regarding procedural rationality, parts of the definition were used to code the data. That means that things like an analytical process, looking for information and alternatives, and relying upon analysis of this information will be part of the rational decision making process. After the data was coded in the labels above, they were all sorted in the dimensions of experience-based processing, emotional-processing, automatic processing and procedural rationality. An example is depicted in table 3.2 below.

Table 3.2 Coding tree for decision making

First order codes (quotes) Second order codes Third order codes “We start with gathering information about the problem to be able to make a good analysis” (C1)

“Basically when I’m informed about the disruption I want to have a clear idea of the situation. So I can discuss internally what should be done” (C3)

17 “We did look at some scenarios or alternatives. As for instance buying the materials at other

suppliers. We looked at reducing our production level for a certain moment” (C3)

“We also looked at a couple of alternatives, but since they are in Asia, it will usually take one to two months until the raw material will arrive” (C3)

Evaluating alternatives Procedural rationality “We try to make the best deal possible, we analyse the alternatives and choose one after

that”(C2)

“At this moment we are looking for alternative technical solutions so we can do some pre-work and start the process later on” (C6)

“So we had to stop the production and start to search for the cause. With using so many raw materials, it’s hard to find the actual cause” (C12)

Thorough analysis

“A lot of those decisions were made based on experience” (C5)

“The reason for calling other CNC machine companies is because we know, based on previous experience, that there is only one company who produces the specific parts for our industry” (C14)

Experience

Experience-based processing “We know that from market research, so we have that knowledge. We have gained that from

factory visits and going to other suppliers” (C5)

“But we know that product supplying isn’t their strength”(C9) Knowledge “I automatically check if we do have enough stock to be able to anticipate for whatever the

consequences of the fire were” (C4)

“We know that were dependable on this specific manufacturer in case something of the CNC breaks down. So we automatically call the manufacturer” (C14)

Automatic

Automatic processing “We immediately knew that it could be a potential impact” (C3)

“So we immediately knew that we had nowhere to go. The switching costs and risk would be too high” (C6)

“It started again with a meeting in the morning, so I immediately looked at our inventory and decided to start one product on another production line” (C12)

Immediately

“What we are currently doing is not very rational, if we have to do a spot buy we do this because we feel the pressure reacting as fast as possible” (C2)

“So there are people who have interactions or know those suppliers. In this case we were a bit desperate” (C7)

“I checked because I had the feeling that this would be the case of the entire batch” (C13)

(gut) Feeling Emotional processing

The same procedures were followed for the different SCRES strategies. First data was reduced and all overlapping descriptions were removed. Accordingly, they were grouped to second-order codes and linked to the different SCRES strategies. Another example is depicted in figure 3.3 below.

Table 3.3 Coding tree for SCRES strategies SCRES

strategies

Deductive codes- Second order codes

First order codes

Flexibility

Options / alternatives “That meant we started to duplicate their product at another company Y” (C1)

“The reason for calling other CNC machine companies is because we know, based on previous experience, that there is only one company who produces the specific parts for our industry” (C14)

“So we immediately knew that we had nowhere to go. The switching costs and risk would be too high” (C6)

“What also helped was having a database at hand in which all information about benchmark pricing was saved” (C1)

Single source Switching costs

Benchmarking

Collaboration

Communication “We were informed by a supplier that they had a problem with their production. That was well communicated” (C3)

“So there’s no collaboration with this supplier, no exchange of data, no sharing of information” (C6)

“Because we had a good deal in the past, you approach them because you have a good feeling about them” (C2)

Information Past performance

Velocity

Time pressure “What we are currently doing is not very rational, if we have to do a spot buy we do this because we feel the pressure and should react as fast as possible” (C2)

“In this situation it was that we took whatever we could. So there are people who have interactions or know those suppliers” (C7)

18 Visibility

Time “I got the information from the supplier. We looked, together with my plant manager, at what the forecasted impact would be” (C3)

“I got the information from the supplier. We looked, together with my plant manager, at what the forecasted impact would be” (C8)

“Basically when I’m informed about the disruption I want to have a clear idea of the situation. So I can discuss internally what should be done” (C3)

Understanding situation

3.4 Data analysis

After all quotes were coded following table 3.2, they were linked to specific steps in the decision making process. How each of the quotes are assigned to which step, can be found in Appendix B. After determining what the decision making process looks like, the different SCRES strategies and their corresponding quotes were analysed in depth. First, it was determined if the quotes used to describe the use of, or came forward from the use of, the different SCRES strategies contained rationality, intuition or a combination of both. The influence of the SCRES strategies were evaluated with regard to how significantly they affected the decision making process by assigning a value to them from low to high. This allowed for a relative comparison between the different decision making processes and the SCRES strategies. Low represents a minor use of this strategy, whereas high means a significant use of the strategy. Accordingly, the same process of assigning a value was applied to the amount of rationality and intuition which came forward from these strategies. In which low represents a minor use of either rationality or intuition and high represents a high use of either rationality or intuition. By assigning the values to both the SCRES strategies and the decision making approach, a relative comparison could be made between the different decision making processes. This allowed to investigate the mechanisms and see how the use of those different SCRES strategies led to the use of either rationality, intuition or a combination of both. The aim of this analysis was to derive patterns for both similarities and differences between the different decision making processes.

19 decision is considered bad. Bad does not necessarily mean that the disruption is not solved, all disruption were solved but it could have been better.

4. Findings

Following the data analysis, valuable insights could be obtained which are in line with the aim of this study to determine how the different SCRES strategies play a role in the decision making process. Data could be extracted in order to answer the research question; How are decisions being made due to different supply chain resilience strategies and what is their outcome?

Table 4.1 Relation between SCRES strategies and decision making

Procedural rationality Experience-based processing Automatic processing Emotional processing High Flexibility • Options / alternatives (C1,

C13) • Time (C1)

• Benchmarking (C1, C13)

Low Flexibility • Complexity of the product (C11, C12)

• Single source (C14) • High switching costs (C6)

High Collaboration • Communication (C3, C4)

• Information (C3, C4) • Past performance (C2, C11)

• Past performance (C2, C11)

Low Collaboration • Limited information (C6,

C8)

• Uncertainty (C8)

• Limited information (C6, C8)

• Uncertainty (C8)

High Velocity • Time pressure (C2, C7)

• Limited information (C2, C7) • Time pressure(C2, C7) • Limited information (C2, C7) • Time pressure (C2, C7) • Limited information (C2, C7) Low Velocity • Time (C3, C4, C5, C6,

C11, C12)

• Complexity of the product (C3, C5, C6, C11, C12) High Visibility • Information availability

(C1, C3, C4, C8, C11, C12, C13)

• Understanding situation (C3, C4, C8)

Low Visibility • Limited information (C5,

C6)

21

4.1 Findings regarding flexibility

Figure 4.1 shows the decisions which come forward from flexibility and that they are linked to options, time, benchmarking and complexity of the product, shown in table 4.1. As can been seen in Figure 4.1, when flexibility increases, the use of procedural rationality increases. On the other hand, if flexibility is low managers use more intuition to make their decision. Mechanisms such as having options, additional time and benchmarking allowed these managers to make a rational decision and will be explained in detail in the following section.

Figure 4.1 Flexibility and decision making

22 and called suppliers to see whether they have the product and against which price, then you choose the most cheap option”. This benchmarking beforehand made them not only spent less time during the information gathering step, but also helped to quickly analyse the information to find alternatives and solutions. These options, benchmarking and additional time made the decision making process more rational, since it made it easier for themselves to thoroughly analyse all available information. On the other hand, if we look at figure 4.1, cases 3-6 and 11,12 and 14 had a low flexibility. All these cases used intuition, especially experience-based and automatic processing, in order to come up with alternatives and solutions. One manager (C6) stated that “So we immediately knew that we had nowhere to go. The switching costs and risk would be too high”. These high switching costs meant that they had to wait before the external company solved their disruption before their own supply chain was back on its normal operating level. “The reason for calling other CNC machine companies is because we know,

based on previous experience, that there is only one company who produces the specific parts for our industry” (C14), shows that they used experience-based processing to come up with alternatives because they are single-sourced. Not having any options leads to using more intuition to have those options as quickly as possible. Case 11 and 12 however, do show more use of rationality even though the flexibility is low. In this case, it seems that the complexity of the product and disruption do not give them any other choice. “because of the modifications and the software we didn’t encounter something like this before. We do have line breakdowns but that’s not software related”.

4.2 Findings regarding collaboration

23 Figure 4.2 Collaboration and decision making

24 case 6 showed what happens when there was no collaboration in terms of limited information. “So there’s no collaboration with this supplier, no exchange of data, no sharing of information” (C6), which led to a disruption in which it took quite some time to assess the actual impact. This also holds for case 8 in which the manager stated that “We were in a situation that they did not know to who they should deliver and what the amount was. Communication didn’t go that well, in the way that they didn’t say they implemented a new IT-system”. These managers used more experience-based processing and automatic processing to analyse the information before determining the impact. So collaboration does help to assess the impact in a more rational way, in which information is available to analyse.

4.3 Findings regarding velocity

Figure 4.3 shows how the decisions were being made as a result of velocity. Velocity has a close connection with the urgency of the disruption. In case the urgency is high, which means making a decision within a couple of hours, the velocity is also high. This is because if the supply chain needs to react fast to the disruption, the decision maker should make fast decisions as well. Mechanisms such as time pressure and limited information play an important role, which will be elaborated below.

25 Following figure 4.3, case 2 and 7 faced a high velocity in which they had to make a decision on the spot to prevent a major short term loss. They had to avoid customers walking away immediately or losing a certain ‘shelf space’ in a retail store if they cannot deliver. One manager (C7) stated for instance that “In this situation it was that we took whatever we could. So there are people who have interactions or know those suppliers”. Because of the limited information, they relied on the experience of people who have experience dealing with certain suppliers. Case 2 stated explicitly that they do not act rational because they feel the pressure, which shows the use of emotional processing:

“

What we are currently doing is not very rational, if we have to do a spot buy we do this because we feel the pressure and should react as fast as possible”. So a high velocity puts a lot of time pressure on the managers and therefore does not allow managers to gather and analyse information thoroughly. They use all dimension of intuition to be able to make the decision as fast as possible. Case 3-6 and 11 and 12 on the other hand, as can be seen in figure 4.3, had a low velocity during the disruption. This meant that they had several weeks, before making a final decision. “we did have multiple days to gather our information” (C11) illustrates this. Because the velocity is relatively low, they have time to perform an analysis and outweigh multiple options before the disruption has to be solved. Another mechanism which might play a role, is the complexity of the product. All cases are in the chemical industry and when a disruption happens, it makes it hard to find the actual cause or change to an alternative supplier. The competition within this market is relatively low because of the high restrictions on these products, and only a few companies are licenced to make these products. Customers do not have many alternatives as well, which makes it harder to switch to another company.

4.4 Findings regarding visibility

26 Figure 4.4 Visibility and decision making

27 however, requires collaboration or a low velocity to do so. Case 5 and 6 had a low visibility and used more intuition because the necessary information was not available. Due to this limited information, they decided to “We solved the problem by postponing it” (C6) because they could not get the necessary information to determine a course of action.

4.5 Findings regarding the outcome of decisions

Regarding the decisions taken in general there are a few interesting findings. All cases mentioned they will use, or already have used, the experience from this disruption in solving the next one. Only one case stated that he made a decision which led to a less satisfying result. All others solved the disruption good enough to a certain standard, in which it was solved without any additional or long-lasting consequences. However, looking at the perceived outcome of the decisions in table 4.2, whether the managers would make the same decisions next time or would change certain decisions, it shows that 3 out of 14 decision processes would be different next time.

Table 4.2 Outcome decisions and decision making method Outcome decisions (same or different next time) Amount of rationality used Amount of intuition used # Amount of disruptions encountered

Case 1 Same High Low Low

Case 2 Same High Medium Low

Case 3 Same High Medium High

Case 4 Same High Medium High

Case 5 Different Medium High High

Case 6 Different Medium Medium High

Case 7 Same Medium High High

Case 8 Same High High High

Case 9 Same Medium High* Low

Case 10 Different Medium Medium Low

Case 11 Same High Low Low

Case 12 Same High Medium Low

Case 13 Same Medium High High

Case 14 Same High High High

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5. Discussion

The goal of this study was to understand how decisions are being made, in terms of rationality and intuition and how the composition of different SCRES strategies affect these decisions. This study provides valuable insights in the decision making process by showing that it contains both types of decision making, and that both types can be valuable when responding to a supply chain disruption. The main findings of this research are shown in figure 5.1, which show a schematic overview of the decision making process. It shows how managers make decisions, using either rationality, intuition, or a combination of both for each step within this process. By really grasping the perspective of the manager during the interviews, information was obtained to determine how and where, within this process of decision making SCRES strategies play in important role. The findings regarding decision making and SCRES strategies make this research able to contribute to decision making in terms of introducing dual process theory to supply chain resilience. Moreover, it shows that SCRES strategies such as flexibility, collaboration and visibility lead to a more rational decision, whereas velocity leads to a more intuitive decision.

5.1 Decision making in SCRES

30 Figure 5.1. Schematic overview of decision making process

Proc ed ur al R at io nal it y Exper ienc e-bas ed pr oce ss ing A ut om at ic pr oce ss ing Em ot ional Proc ess ing Disruption Asses Impact - Visibility provides access to necessary information - Collaboration helps with providing necessary information Urgency Gathering information - Low velocity provides time for information gathering and analysis Low Make decision on the spot - High velocity takes away time for information gathering and analysis High Regularity Low High Thorough analysis to develop alternatives/solutions - Visibility provides access to necessary information

- High flexibility enables thorough analysis by having information ready

which also seems to be applicable in responding to a disruption. All their dimension of intuition played a role during the decision making in response to a disruption. Intuition can therefore be considered to complement rational decision making, instead of being used on itself. This is in line with dual process theory, which assumes two types of decision making who work together (Evans, 2007). System I can be considered intuition, which works faster compared to system 2, which is a more analytical system (rationality). Current decision making literature also states that decision making should contain a mix of both analytical and intuitive approaches (Hodgkinson et al., 2009; Sadler-Smith & Shefy, 2004). It is therefore proposed that:

P1: Decision making in response to a supply chain disruption consists of both rationality and intuition.

5.2 Decision making and SCRES strategies

32 existing literature, it is in line with the general assumption of intuition in which past experiences are used during decision making (Burke & Miller, 1999). Having flexibility and therefore having alternatives or options ready is something that has not been mentioned before in decision making with regard to supply chain literature. What literature does state is that flexibility will improve the response of a disruption (Sheffi & Rice Jr., 2005), and this study showed how this improves the decision making within that response. Since these options are evaluated and analysed beforehand, it is a step during the rational decision making process which does not need extensive analysis anymore. Doing this beforehand allows a better response since additional time for analysis is not necessary. It seems that a supply chain which put emphasis on these three strategies, is able to respond to a supply chain disruption in a more rational way. Therefore, it is proposed that regarding flexibility, collaboration and visibility:

P2a: The higher the flexibility of the supply chain, the higher the level of rationality in the decision making process in response to a supply chain disruption.

P2b: The higher the collaboration of the supply chain, the higher the level of rationality in the decision making process in response to a supply chain disruption.

P2c: The higher the visibility of the supply chain, the higher the level of rationality in the decision making process in response to a supply chain disruption.

Velocity has a close relationship with time-pressure, which already has been indicated as an important determinant for the use of intuition (Carter et al. 2017; Kahneman & Klein, 2009; Kaufmann et al., 2012). This is in line with the findings in figure 5.1, which shows that extreme time pressure (making a decision within a couple of hours) leads to an increase of intuitive decision making. The high time pressure and information uncertainty make up for the fact that information gathering and thorough analysis is simply not possible. It is therefore proposed that:

33

5.3 Decision making and perceived outcome

Regarding the perceived outcome of decisions it was found that rationality seems to be a prerequisite for having the best perceived outcome when responding to a supply chain disruption. This perceived outcome of rational decisions is in line with previous research (Kaufmann et al., 2012), which leads to a higher decision performance. Intuition, which is said to lead to better decision outcomes when faced with time pressure and uncertainty (Dane & Pratt, 2007), cannot be fully confirmed or denied. However, managers do acknowledge the use of intuition and address the importance of using their experience and gut feelings in making a decision, which is also confirmed by the study of Khatri and Ng (2000) and Salas, Rosen and DiazGranados (2010). It is therefore suggested that both rationality and intuition should be part of the decision making process to obtain the best perceived outcome, which leads to the following proposition:

P3: Decisions in response to a supply chain disruption should contain both rationality and intuition to have the best perceived outcome.

6. Conclusion

34

6.1 Managerial implications

Besides addressing this gap in literature about rationality versus intuition in SCRES, it also provides some valuable managerial insights. Managers can benefit from this research by knowing that decisions in response to a supply chain disruption are not fully rational, nor do they have to be. It shows that having experience with handling supply chain disruptions is a valuable skill, and most of the time lead to good decisions. However, it is advised to maintain some level of rationality during these decisions, since there is evidence that this is an important prerequisite for having a good perceived outcome of those decisions. The schematic overview allows them to see which steps, and how these steps can be improved, might lead to an improvement in their overall decision making in response to a supply chain disruption. It provides guidance to managers on how SCRES strategies can be used to improve their decision making. A strategy configuration of flexibility, collaboration and visibility might lead to more rational decisions. However, having a high velocity enhances the use of intuition in decision making. This also shows managing directors that, if possible, experienced managers should make decisions and are a valuable asset to their company. Moreover, it also shows them that deviations from a standard procedure during a crisis when there is limited information available, does not necessarily has to be a bad thing.

6.2 Limitations and future research

36

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42

Appendix

A: Interview protocol (background, purpose, consent form and questions)

Introduction

Supply chain disruptions are inevitable. Therefore, resilience as the ability of a supply chain to

respond and recover from events that impact performance is on the agenda of many

organizations. During this process of responding to adverse events, the decisions of the supply

chain manager determine success or failure. This decision making could be influenced by

personality, experience, available tools and/or emotions, to name just a few factors. While the

strategies that help to be more resilient are often known (e.g., spare capacity, extra inventory,

second supplier), how managers derive at these decisions remain unclear. That is why we are

interested to explore and gather insights into the decision making process of managers during

disruptions.

I would like to ask for your help in answering this question and a little bit of your time. I would

like to conduct an interview (approximately 60 minutes) in November or December with an

experienced supply chain manager in your company. In return for your help, you will receive a

management report of the findings of all interviews and will be invited to the final presentations

of the results in February.

Procedure of the Interview

Questions for the interview can be found on pages 3 to 5 of this document and relate to two

specific supply chain disruptions that happened in the last year. To enable a smooth process, I

would like to ask you to identify these disruptions before the interview and complete a short

questionnaire (pages 3-5) that will allow me to guide the interview. If you have any questions

regarding the questions or interview in general, please do not hesitate to contact me. Also after

the interview, there will be room for feedback and further things you might want to discuss.

With your permission, I would like to record the interview. The audio files are available for the

researcher only and will not be redistributed. All information gathered from you and your

company will be treated confidential and anonymous. You are not obliged to answer all

questions and can withdraw from the interview at any time. In order to maintain these common

interests and values, I would like to ask you to fill in a confidentiality form at the beginning of

the interview (page 2).

About the researcher

I am Tony Mullié, 25 years old and Supply Chain Management Master student at the University

of Groningen. Right now I am conducting research regarding my Master Thesis and aim to

finish it at the end of January.

Thank you in advance and I am looking

forward to hearing from you.

Yours sincerely,

Tony Mullié

Contact Details

Researcher

Supervisor

Tony Mullié

Dr. Kirstin Scholten

a.r.mullie@student.rug.nl

k.scholten@rug.nl

43

Researcher’s Name: Tony Mullié

Faculty/School/Department: Faculty of Economics and Business, University of Groningen Field of Study: Research project on decision making in supply chain resilience

Location of interview:

Function/ organization of interviewee: Time at start:

To be completed by the interviewee:

1.1 Have you been fully informed/read the information sheet about this study?

1.2 Have you had an opportunity to ask questions and discuss this study?

1.3 Have you received satisfactory answers to all your questions?

1.4 Do you understand that you are free to withdraw from this study? • at any time

• without giving a reason for withdrawing

• without affecting your future relationship with the institute

1.5 Have you been informed that this consent form shall be kept in the confidence of the researcher?

1.6 Do you agree that the interview will be recorded?

1.7 Do you agree to be quoted in the main text anonymously?

1.8 Do you agree to take part in this study, of which the results are likely to be published? (Please note: no information will be traced back to you).

1.9 If there are any other preferences or restrictions you would like to mutually agree upon, please write them down below. In case there are no other preferences or restrictions, please leave this open:

_________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ YES/NO YES/NO YES/NO YES/NO YES/NO YES/NO YES/NO YES/NO Signed_____________________________________________________ Date __________________

Name in Block Letters ________________________________________________________________