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On uncertainty in supply chain risk management
On uncertainty in supply chain risk management
Jyri Vilko, Paavo Ritala and Jan Edelmann School of Business, Lappeenranta University of Technology, Lappeenranta, Finland
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Received 31 October 2012 Revised 2 April 2013 2 September 2013 Purpose – The concept of uncertainty is a relevant yet little understood area within supply chain risk Accepted 27 September 2013
Abstract
management. Risk is often associated with uncertainty, but in reality uncertainty is a much more elaborate concept and deserves more in-depth scrutiny. To bridge this gap, the purpose of this paper is to propose a conceptual framework for assessing the levels and nature of uncertainty in this context. Design/methodology/approach – The aim of the study is to link established theories of uncertainty to the management of risk in supply chains, to gain a holistic understanding of its levels and nature. The proposed conceptual model concerns the role of certainty and uncertainty in this context. Illustrative examples show the applicability of the model. Findings – The study describes in detail a way of analysing the levels and nature of uncertainty in supply chains. Such analysis could provide crucial information enabling more efficient and effective implementation of supply chain risk management. Practical implications – The study enhances understanding of the nature of the uncertainties faced in supply chains. Thus it should be possible to improve existing measures and analyses of risk, which could increase the efficiency and effectiveness of supply chain and logistics management. Originality/value – The proposed conceptual framework of uncertainty types in the supply chain context is novel, and therefore could enhance understanding of uncertainty and risk in supply and logistics management and make it easier to categorise, as well as initiate further research in the field. Keywords Uncertainty, Risk, Supply chain risk management, Conceptual framework Paper type Conceptual paper
1. Introduction Complexity, specialisation, and disintegration are emerging as major challenges in terms of risk management in supply chains, having made them vulnerable to disturbances from both inside and outside the system. Indeed, many recent events have shown how vulnerable long and complex supply chains are. Such events include the well-known melamine crisis in Chinese dairy products, and other food crises; major natural disasters including floods, tsunamis, and earthquakes; industrial and societal disputes across the globe, as well as firm and supply chain-specific glitches and disturbances (Hendricks and Singhal, 2003; Chopra and Sodhi, 2004; Sheffi, 2005; Narasimhan and Talluri, 2009). This vulnerability has attracted the attention of many academics in the field of logistics and supply management, in which risk-related issues are increasingly taken into account (Minahan, 2005; Kleindorfer and Saad, 2005; Sanchez-Rodrigues et al., 2008, 2010; Wagner and Neshat, 2010; Ghagde et al., 2012). In this context, the quality and competitiveness of individual companies’ operations depend on their ability to identify and mitigate the uncertainties and risks they encounter. However, although awareness of vulnerability and of risk management is increasing among academics and practitioners, many related concepts are still in their infancy. There are thus insufficient conceptual frameworks and This paper is based on a paper presented at the 17th International Symposium on Logistics (www.isl21.net) held in July 2012 in Cape Town, South Africa.
The International Journal of Logistics Management Vol. 25 No. 1, 2014 pp. 3-19 r Emerald Group Publishing Limited 0957-4093 DOI 10.1108/IJLM-10-2012-0126
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empirical findings to provide a clear picture of the phenomenon of supply chain risk management ( Ju¨ttner, 2005; Manuj and Mentzer, 2008). Accordingly, both academic research and practitioner reports stress its importance as well as the need to develop different approaches (Blos et al., 2009; Manuj and Mentzer, 2008; Shaer and Goedhart, 2009). In line with the above-mentioned developments, the role of supply chain risk management has recently been increasingly emphasised. Indeed, Ju¨ (2005) found that 44 per cent of organisations expected their vulnerabilities to increase within the next five years. More recently, the need for risk management has become evident following Snell’s (2010) study showing that 90 per cent of the respondent companies feared supply risks, whereas only 60 per cent felt confident or knowledgeable enough to cope with them. In addition, Hendricks and Singhal (2005) found out that firms that experience supply chain disruptions experience on average 40 per cent stock returns. Thus, it is not surprising that there is a growing interest in supply chain-related decisions and the uncertainty and risk involved (Prater, 2005; Swink and Zsidisin, 2006; Craighead et al., 2007; Hendricks et al., 2009; Hult et al., 2010). In fact, it has traditionally been assumed that supply side risk is similar to or equivalent to demand side risk (Christopher and Peck, 2004). However, it has been suggested that supply-side risks and uncertainty are much more complex issues than demand-side ones, and that, therefore, managing them needs more careful attention (see Snyder and Shen, 2006). Supply chain risk management concerns risk as a situation entailing exposure to two essential components: an event and the uncertainty concerning the possible outcomes (Holton, 2004; Sheffi, 2005). Thus, risk assessment is based on the likelihood of the occurrence of the risk situation, and on the kind of damage it might entail if realised (Mitchell, 1995). The different types of risk are extensively covered in the relevant literature (Rao and Goldsby, 2009), but analysis of the concept of risk itself is very limited. For instance, the management of risk is commonly discussed in terms of vulnerability and uncertainty (Sorensen, 2005), meaning that the concept is understood as an occurrence for which the probability distribution is known. However, we suggest that in reality, it is much more common that the probability distributions cannot be defined. Thus, in order to enhance understanding of how risks could be better managed under such conditions, we put forward a conceptual framework focused on the role of uncertainty in supply chain risk management. Supporting our argument and illustrating the research gap, some authors have criticised the fact that the literature on supply chain risk management does not always clearly distinguish between risk and uncertainty, which makes the definitions quite vague (Tang and Nurmaya Musa, 2010). Further, it has been suggested that merely recognising uncertainty is not enough, but researchers and practitioners need more rigorous frameworks breaking uncertainty down into more detailed elements (Prater, 2005). Indeed, earlier studies have reported that the concepts of risk and uncertainty are less understood and developed in this context than in other disciplines (Khan and Burnes, 2007). Given that the two concepts are among the most essential in supply chain risk management, clarity is of the essence. The aim in this paper, therefore, is to enhance understanding of these concepts by illustrating the levels of uncertainty in supply chain risk management. In so doing, we hope to define the nature of uncertainty so as to facilitate a more comprehensive and valuable consideration of how the concept should be approached in future studies in the field. In the next section, we introduce the theoretical background of supply chain risk management and uncertainty. We then develop our conceptual model of different types
of certainty/uncertainty and their implications for the analysis and management of supply chain risk. In building the framework, we utilise an integrative literature review, aiming to synthesise and integrate existing literature in the quest of generating new frameworks and perspectives (Torraco, 2005). We mainly build on the existing supply chain risk management literature and combine it with the insights from economic theory on certainty vs uncertainty (Simon, 1957; Langlois, 1984; Dosi and Egidi, 1991). Illustrative examples of each type of uncertainty follow, which cover events related to the focal actor, the supply chain, and the external environment. The study ends with discussion of the theoretical and practical implications and further research directions. 2. Theoretical background 2.1 Supply chain risk management The supply chain comprises a series of activities and organisations through which material and information move on their way to the final customer. Peck (2005) describes supply chain vulnerability in this context as exposure to serious disturbance arising from risks within and external to the chain. According to Waters (2007), vulnerability reflects the susceptibility of a supply chain to disruption, and is a consequence of risks in it. Ju¨ (2005) further refers to supply chain vulnerability as the propensity of risk sources and drivers to outweigh risk-mitigating strategies, thus causing adverse consequences in the chain and jeopardising its ability to effectively serve the end customer market. Supply chain risk management, in turn, is a function that aims to identify the potential sources of risk, and to implement appropriate actions to avoid or contain supply chain vulnerability (Narasimhan and Talluri, 2009; see also Ghagde et al., 2012). Supply chain risk is commonly portrayed as a threat that something might happen to disrupt normal activities and that would stop things happening as planned (Waters, 2007). Most of the literature defines risk as purely negative and leading to undesired results or consequences (Harland et al., 2003; Manuj and Mentzer, 2008). A standard formula for the quantitative definition of supply chain risk is (Mitchell, 1995): Risk ¼ PðLossÞ I ðLossÞ where risk is defined as the probability ( P ) of loss and its significance (I). Hetland (2003) and Diekmann et al. (1988) view risk as an implicitly uncertain phenomenon. It should be noted that there are differences between the two concepts of risk and uncertainty, however. Waters (2007) explains the difference, suggesting that a risk occurs because there is a certain type of uncertainty about the future. In this traditional risk management context, uncertainty means that unexpected events may occur, but they can be quantified and therefore managed. However, as a concept, uncertainty reaches far beyond the traditional conception of risk, and thus it deserves more elaborate scrutiny. Van der Vorst and Beulens (2002, p. 413) define supply chain uncertainty as “decision making situations in the supply chain in which the decision maker does not know definitely what to decide as he is indistinct about the objectives; lacks information about its environment or the supply chain; lacks information processing capacity; is unable to accurately predict the impact of possible control actions on supply chain behaviour; or, lacks effective control actions.” The sources of uncertainty include quantity, quality, and time (Van der Vorst and Beulens, 2002). Davis (1993) was among the first scholars to explicitly consider uncertainty as a strategic issue for supply chains. However, in his investigations, the sources were
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limited to suppliers, manufacturing, and customers, and the effects to the performance of the supply chain. Later, building on the work of Davis (1993), Mason-Jones and Towill, 1998 developed an uncertainty circle model, adding control systems as one more source and offering a wider perspective on the supply and demand sides of the chain; this was then further complemented by Geary et al. (2002) and Sanchez-Rodrigues et al. (2008). It appears from these studies that uncertainty has been taken into account in supply chain risk management in various ways and in different contexts, but the literature still lacks frameworks and consensus in terms of the role of uncertainty. 2.2 Uncertainty and risk Knight’s (1921) distinction between certainty, risk, and uncertainty could be regarded as the best-known and most frequently used typology of uncertainty in risk management. In his definition of risk, he coined the terms measurable uncertainty (quantitative) and unmeasurable uncertainty (non-quantitative), when only partial knowledge of outcomes such as beliefs and opinions is available. The measurable, quantifiable perspective portrays a type of “basic uncertainty” that can be managed using objective measures, whereas “Knightian uncertainty” refers to immeasurable risks that cannot be calculated. We adopt this intuition as our starting point in developing a framework for uncertainty in supply chain risk management. In this context, Trkman and McCormack (2009) classify uncertainty in two categories, endogenous and exogenous, depending on whether they derive from within or outside the supply chain. Ju¨ttner et al. (2003) also suggest including external uncertainty in other sources of uncertainty in the chain. The exogenous-endogenous distinction in itself, however, is too vague to make sense of how uncertainty really affects decisions related to supply chain risk management: it describes the source of uncertainty, which is relevant as such, but it does not describe the type of uncertainty. Thus, we propose that uncertainty, especially in the context of supply chain risk management, could be examined through the lenses of substantive and procedural uncertainty, as explained below. In line with Simon’s (1957) concept of rationality, Dosi and Egidi (1991, pp. 145-146) introduce the notions of substantive and procedural uncertainty. Substantive uncertainty derives from the “incompleteness of the information set” and is related to a “lack of information about environmental events” and “all the information which would be necessary to make decisions with certain outcomes”. A similar notion has been discussed in the project management context by De Meyer et al. (2002), who discuss unforeseen uncertainty, which refers to the lack of ability to predict factors that influence project-related risks. Procedural uncertainty arises “from the inability of the agents to recognise and interpret the relevant information, even when available”. It concerns “the competence gap in problem-solving” and “limitations on the computational and cognitive capabilities of the agents to pursue unambiguously their objectives, given the available information”. Uncertainty in a supply chain could be classified as illustrated in Figure 1. The main components used to distinguish between types of uncertainty include the following: (1)
the knowledge level of the decision maker related to the problem under each type of uncertainty;
On uncertainty in supply chain risk management
CERTAINTY
Knightian basic uncertainty
RISK
7 UNCERTAINTY
SUBSTANTIVE
PROCEDURAL
Related to the outcomes Environment-dependent
PARAMETRIC
Related to the decision process Decision-maker dependent
STRUCTURAL
COMPLEXITY
(2)
the decision-maker’s knowledge of the possible actions in which to engage;
(3)
the decision-maker’s knowledge of possible states of the world;
(4)
the decision-maker’s knowledge of the consequences resulting from the interaction between actions and states of the world; and
(5)
the decision-maker’s subjective or objective knowledge of the probabilities of the occurrence of possible states of the world.
This classification distinguishes three types of uncertainty: parametric and structural (i.e. environment dependent) and procedural (i.e. decision-maker dependent). We believe this distinction gives a valuable perspective on uncertainty related to decision making (Langlois, 1984; Dosi and Egidi, 1991; Kyla¨heiko, 1995; Kyla¨heiko et al., 2002). Within each of these categories, the decision maker has a different amount of knowledge about the state of the world and its events, and therefore also has different kinds of resources with which to cope with uncertainty. At the extreme, uncertainty could also be conceptualised as radical, when all pieces of knowledge are imperfect and there is no knowledge about the structure or probability of future events (Loasby, 1976; Kyla¨heiko, 1995). 3. Conceptualising (un)certainty in the supply chain context Supply chain risk management is assumed to handle risks either by proactively mitigating them or by reactively responding to them (Chopra and Sodhi, 2004; Tomlin, 2006; Ghagde et al., 2012). The current literature on supply chain risk commonly views all threats disrupting normal activities (i.e. risks) as products of the impact and the probability of an event (March and Shapira, 1987; Manuj and Mentzer, 2008; Vilko and Hallikas, 2012), which in reality may be impossible to measure. Thus, we suggest that the nature of uncertainties plays a crucial role here, since it affects the visibility and the possibilities of decision makers within a certain domain (see De Meyer et al., 2002;
Figure 1. Certainty, risk, and uncertainty
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Prater, 2005). Thus, if supply chain management is to accomplish its tasks in both theory and practice, it has to understand the concept of uncertainty in its entirety. Given that the environment cannot normally be fully controlled, there are always unknown elements facing decision makers. In general, decision makers in firms need to cope with decision-making variables that involve variation within a predictable range as well as events that are close to chaos or crisis (Weick, 1993; Pearson and Clair, 1998; De Meyer et al., 2002). Taking into account all the influential environmental factors is therefore impossible, and the information on the basis of which probabilities are formed is more or less imperfect. In light of the above discussion on various types of uncertainty, Table I explains in more detail the range between (complete) certainty and radical uncertainty and the implications for supply chain risk management. Table I describes the amount of knowledge that decision makers hold about the state of the world (here: the supply chain) and its events. The amount of knowledge thus describes the decision maker’s ability to cope with uncertainty. Complete certainty, as illustrated in the first column, is an example of a hypothetical world in which all relevant information is known to the decision maker. In terms of supply chain risks, it suggests a situation in which all related risks (inside and outside of the chain) and their consequences are known. Thus, in such a situation, there are no actual risks, because all of them can be mitigated. In reality, this situation is, of course, impossible. The second column describes a situation in which the structure of future events is known in terms of objective probabilities concerning the likelihood and impact of their occurrence. This probabilistic certainty is typically regarded as an implicit basis for the analysis of supply chain risk and the subsequent management actions. However, typically the level of knowledge required for objectively assessing the likelihood and impact of risk events is rarely known, which is why various types of uncertainty need to be addressed as well. The third column illustrates a situation in which the structure of the future (or future event) is completely known but the probability parameters are not. The uncertainty here is environment dependent. This situation is referred to as parametric uncertainty, and it includes only subjective beliefs about the probabilities of future events and their outcomes (Langlois, 1984; Dosi and Egidi, 1991). In terms of supply chain risk management, potential risk events are identified, but in terms of likelihood and impact, they are difficult to assess objectively – the risk analysis is based on subjective assessment. Parametric uncertainty allows quantification of different aspects of the events; however, the following categories of uncertainty do not, and they can only be described (assessed) qualitatively. The fourth column indicates environment-dependent structural uncertainty. Knowledge related to the state of the world in the future is imperfect, and only subjective beliefs can be projected (Langlois, 1984). In terms of supply chain risk management, this means that no holistic picture of the supply chain and the related risk events can be objectively formed. The probabilities of the identified events are also difficult to quantify, and the interdependence related to the operations of the supply chain is unclear. Therefore, the analysis cannot objectively assess the risk events or their causality. The fifth column in the table illustrates procedural uncertainty, meaning that the decision maker is constrained by his or her computational and cognitive capabilities (Dosi and Egidi, 1991) and therefore cannot form a clear picture of the processes or the risk events, mainly on account of their complexity. Inadequate cognitive abilities and
The risk events and their causalities are not fully known or assessable The risk events and their causalities cannot be objectively assessed
The risk parameters (likelihood and impact) cannot be objectively assessed
No risk analysis is needed
The structure of the supply chain and the related risks are difficult to formulate
Risk probabilities are difficult to quantify
Probability and impact of risks assumed and planned for with specific certainty (most common approach for supply chain risk management) The risk parameters (likelihood and impact) can be measured and assessed with certainty
Complete certainty about the supply chain and related risks (hypothetical world)
Implications for supply chain risk analysis
Severely restricted visibility of the supply chain and related risks
Subjective beliefs
Subjective degrees of beliefs as to the probabilities of events and the consequences of one’s actions
Objective knowledge of probabilities
Complete knowledge
Knowledge of the occurrence probabilities of possible states of the world, possible actions and consequences Implications for supply chain risk management
The limitations of the decisionmaker’s cognitive abilities unambiguously to pursue their objectives given the available information Incomplete knowledge about events
Imperfect knowledge about how the future will turn out
The structure of the future is known. The probability parameters are not certain
Future states and the structure of the decision situation are known. The probability of each future event is objectively known
Every piece of relevant knowledge is known
Procedural uncertainty
Structural uncertainty
Parametric uncertainty
Probabilistic certainty (risk)
The knowledge the decision maker holds related to the problem
Certainty
(continued)
The risk events and parameters cannot be assessed at all
Complete uncertainty about the supply chain and related risks (hypothetical world)
No knowledge at all
All pieces of knowledge are imperfect, sometimes even coming close to ignorance
Radical uncertainty
On uncertainty in supply chain risk management 9
Table I. On defining uncertainty in supply chains
Table I.
Decision-maker acts upon complete knowledge and implements perfectly effective activities
Radical uncertainty Decision maker cannot act or perform rationally
Procedural uncertainty Decision-maker acts fully upon subjective intuition about future events and attributes
Structural uncertainty Decision-maker acts upon subjective assessment of both event structure and probability and their impact
Parametric uncertainty Decision-maker acts upon known event structure and subjectively evaluates how probable and impactful different events are
Probabilistic certainty (risk) Decision-maker acts upon known event structures and attributes based on accurate calculations and predictions
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Implications for decision maker (e.g. supply chain/ logistics manager)
Certainty
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imperfect knowledge related to future events severely limit the decision maker in pursuing set objectives. In the context of supply chain risk management, this reflects a situation in which the supply chain has severely limited visibility in terms of its activities and the related risks. Only a fraction of the events and risk can be identified and analysed – and then merely subjectively. Further, structural and parametric uncertainties may be associated with procedural uncertainty; the sources of uncertainty that allow some actors to generate unforeseeable changes are endogenous sources of uncertainty for others (Dosi and Egidi, 1991). The last column, radical uncertainty, refers to a hypothetical world in which there is total imperfection in terms of knowledge (see Prater, 2005, on “chaotic uncertainty” for parallel discussion). Thus, supply chain risk management and analysis are impossible in that all knowledge related to each decision-making element is incomplete, which does not even allow the formation of subjective beliefs about future events. This type of uncertainty is unlikely in any situation, given that subjective beliefs may be based on a very limited understanding of events and their context. 4. The applicability of the framework By way of a practical illustration of the application of the developed framework in supply chain risk management, we follow typical examples of risk through the different uncertainty categories (Table II). We distinguish between events related to the focal actor, the supply chain, and the external environment (which is a widely adopted distinction in the literature, e.g. Trkman and McCormack, 2009). With regard to events related to the focal actor, we illustrate the risk of industrial unrest (employee strike affecting the focal actor’s activities). This risk is quite easily mitigated on the left-hand side of the framework, with complete certainty and probabilistic certainty, because management can prepare for known situations in which employee strikes threaten supply chain and logistics operations. In contrast, moving towards more radical forms of uncertainty, the related factors and causalities start to become more unclear, and their assessment more subjective. Managing risks is likely to be a much thornier task in these situations. Turning to events related to the supply chain, we give the example of the crash (operational failure/malfunction) of supply chain information systems, which is an increasing concern in supply chain and logistics operations. When there is a probabilistic certainty (a typical definition of risk assessment), it is possible to prepare for the crashes and failures, and the risks can be sufficiently mitigated. However, on the right-hand side of the framework, events leading to information system crashes may not be understood or recognised, which makes preparing for them very difficult, if not impossible. In terms of the external environment, our example relates to a sudden and impactful economic crisis. In conditions of certainty, there are no risks, because there is perfect information regarding the economy and related factors. Although this is definitely not realistic, probabilistic certainty (i.e. the traditional risk-assessment category) is not a very likely scenario either, in the long run. In fact, economic situations may very likely involve parametric uncertainty issues such as unanticipated fluctuations in supply and demand, which may have an impact that can only be subjectively assessed. These situations may also involve even more uncertainty that is not fully understood and is hard to define (e.g. in emerging economies). These examples show the importance of analysing the nature of uncertainty in supply chain and logistics operations. In order to effectively manage the risks involved,
On uncertainty in supply chain risk management 11
There is no knowledge about the structure of the economic system nor the causalities leading to potential crisis situations from supply chain point of view The factors affecting economic environment and its effect on supply chain/network are known, but their likelihood and impact are unclear and are based on subjective intuition
The known relative probability of factors affecting the balance of the economic environment, based on historical data or historically accumulated experience
Completely accurate knowledge about factors affecting economic environment of the supply chain/ network and its potential fluctuation and vulnerability
Events related to the external environment (economic crisis)
(continued)
The economic environment is deemed as completely external and cannot be assessed or analysed at all
Changes in system status are not noticed or recognised The system operability and malfunctions are recognised, but neither the factors leading to them nor their causalities are fully understood, and they are hard to define The mechanisms and factors of the economy and economic crisis are not fully understood and are hard to define, and their linkage to the supply chain is not fully understood
The factors affecting system operability and malfunctions are known, but their likelihood and impact are unclear and are based on subjective intuition
The known relative probability of system malfunctions and operability
Completely accurate knowledge about current systems status and future changes in it
Events related to the supply chain/ network Supply chain information systems crash (operation failure/ malfunction)
The industrial unrest is not understood or recognised as a phenomenon
The factors and causalities related to industrial unrest are not fully understood and are hard to define
The factors related to industrial unrest are known, but their effects are unclear and are based on subjective intuition at best The factors related to changes in system operability and malfunctions can only be assessed subjectively
The factors leading to industrial unrest are known, but their likelihood and impact are unclear and are based on subjective intuition
The relative probability of industrial unrest is known, and related factors can be calculated accurately
Radical uncertainty
Procedural uncertainty
Structural uncertainty
Parametric uncertainty
Completely accurate knowledge about workers’ and unions’ motivations, plans and activities
Table II. Illustrative examples of using the developed framework Probabilistic certainty (risk)
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Events related to the focal actor Industrial unrest (employee strike)
Certainty
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Key managerial activities
No activities needed related to supply chain risk management – design of supply management is based on full certainty
Certainty
Procedural uncertainty Simplify, categorise, and evaluate risk events and situation based on available information and experience, and implement effective risk management activities based on those
Structural uncertainty Evaluate probabilities and risk situation based on available information and best understanding for each recognised risk event and context and implement effective risk management activities based on those
Parametric uncertainty Evaluate probabilities based on available information and best understanding for each recognised risk event and implement effective risk management activities based on those
Probabilistic certainty (risk) Calculate (objectively) probabilities for each recognised risk event and implement effective risk management activities based on those
No activities based on risk management knowledge can be executed due to complete uncertainty
Radical uncertainty
On uncertainty in supply chain risk management 13
Table II.
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management needs to pay close attention to the certainty and uncertainty levels in the parameters, structures, and processes related to supply chains (as described in the bottom row of Table II). If the certainty level is low (e.g. procedural uncertainty) with regard to a major event, there is a need for activities that enhance it, or the organisation needs otherwise to accept the consequences of being exposed to a level of uncertainty. When the uncertainty increases, the risk management activities should be directed more and more towards making sense of the situation and of risk events themselves, as the level of subjectivity and intuition rises. Therefore, it is relevant to understand both the level of objective information and the understanding used in subjective decision making in the supply chain risk management context. 5. Discussion and implications Supply chains have become very long and complex, with many parallel physical and information flows to ensure that products are delivered in the right quantities, to the right place, in a cost-effective manner (Ju¨ttner, 2005). At the same time, they are becoming more and more vulnerable to serious disturbances. In line with these developments, supply chain risk management and the uncertainty related to it are assuming increasingly important roles. However, the concept of uncertainty is fuzzier in the area of supply chain risk management than in many other disciplines, a research gap that has been identified in some existing studies (Tang and Nurmaya Musa, 2010; Sorensen, 2005). In fact, in a recent systematic literature review (Ghagde et al., 2012), it was suggested that further research is needed to better understand the visibility and traceability of risks, which is still an underdeveloped area in the field of supply chain risk management. Therefore, in order to enhance understanding on these issues, we propose a conceptual framework and offer implications illustrating the various levels of uncertainty in the supply chain context. We categorise the level of uncertainty in more detail than in previous studies, which enables the construction of a more effective and realistic risk management strategy with a better understanding of the level of known information and the nature of uncertainty related to it. 5.1 Implications for the literature on supply risk management Currently, supply chain risk management tends to assess risks as if they were based on objective measures (e.g. Bogataj and Bogataj, 2007; Yang, 2011), which in this paper we refer to as probabilistic certainty. However, in reality this objective basis is rarely the case. Therefore, the measures derived from such assessments may well be based on subjective beliefs, and thus should be treated as such. This carries major implications in terms of risk management and risk analysis in supply and logistics chains and networks. The current contributions on uncertainty in supply chain risk management are still quite scarce, and only a few scholars have studied the phenomenon. Typically, the presented frameworks (e.g. Mason-Jones and Towill, 1998; Wilding, 1998; Prater et al., 2001; Sanchez-Rodrigues et al., 2008; Van der Vorst and Beulens, 2002) concentrate on studying the identification of and controlling the uncertainty from different parts of the supply chain process, as well as studying the effectiveness implications. However, these studies do not address in detail the nature of uncertainty itself. Thus, a gap can be identified in the current body of knowledge, and this is the gap at which the core argument of this study aims. There is, however, some work that comes close to our aims here. For example, in the project management context, De Meyer et al. (2002) underline the need for a better understanding of uncertainty, and analyse the degree of
what is known and what can be planned and done based on that. Similarly, we analyse the uncertainty in the supply chain risk management domain from the perspective of the decision maker, and we concentrate on the level of knowledge that can be used for the basis of identification, analysis, and management of the uncertain events. In building our argument, we have combined various theories of uncertainty (Knight, 1921; Dosi and Egidi, 1991) in the context of supply chain risk management. The levels of uncertainty presented in the developed framework range between complete certainty and complete (radical) uncertainty. In particular, risk is inherent in uncertainty (i.e. probabilistic certainty), whereas, in the literature, uncertainty is commonly seen as inherent in risk. The view presented here thus differs from that expressed in the mainstream literature in that regard, and it facilitates deeper exploration of the concept of uncertainty and its implications. Our uncertainty framework for supply chain risk management contributes to the existing literature in two distinct ways. First, it illustrates the different levels of uncertainty and therefore enhances understanding of its nature when risks are assessed and managed. This could help researchers in the field to better assess the available information on risks, and on this basis to make stronger recommendations with regard to how such risks should be managed. There is existing literature addressing the different levels of uncertainty in the project management context (e.g. De Meyer et al., 2002; Ward and Chapman, 2003). In addition, there is research focusing on uncertainty in terms of its sources such as demand and forecasting uncertainty (Prater et al., 2001), as well as on the generic influence factors on the level of uncertainty (Prater, 2005). Complementing these approaches, our work takes one step further in explicating the nature of certainty and uncertainty in the supply management context through the lenses of parametric, structural, and procedural dimensions. This allows for even more in-depth inquiry into the nature of the phenomenon, helping to grasp its dimensions as well as their interrelations. Thus, as the existing frameworks treat uncertainty mostly as a one-dimensional phenomenon and identify its sources and implications (Mason-Jones and Towill, 1998; Prater et al., 2001; Sanchez-Rodrigues et al., 2008), our study can be used to complement these frameworks by illustrating the nature of different uncertainty types and levels. Second, the framework carries implications for decision making under various levels of uncertainty, which should help decision makers to design better information and other support systems in and beyond the context in question (see e.g. Mansouri et al., 2012). In particular, in focusing on the type of knowledge the decision maker has or lacks, it highlights the potential strong points as well as the shortcomings in the decision-making process. 5.2 Practical implications In practical terms, analyses of the nature of uncertainty could provide crucial information for supply chain risk management and therefore could enable more efficient and effective implementation. The framework opens up new insights and could be useful in the risk mitigation process to organisations attempting to assess their vulnerability to different exogenous and endogenous events. In particular, the better that management understands the nature of uncertainty, the easier it is to allocate resources related to supply chain risk more effectively. When uncertainties concerning an important event are more procedural in nature, more resources should be allocated to providing more clarification. Despite the inevitable restrictions on the possibility of reducing uncertainty, resource allocation and information gathering will ease at least some of it. An organisation making such
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decisions should examine the cost/benefit implications of accepting certain levels of uncertainty or investing in lowering the level when necessary. In fact, a multidimensional management approach for supply risk management has been suggested in order to effectively and efficiently handle various types of risks in the firm’s supply chain (Chopra and Sodhi, 2004; Kleindorfer and Saad, 2005). Utilising the uncertainty types introduced in this study may help decision makers in further assessing the best possible risk mitigation activities and processes. Finally, the existing practice-oriented models for supply chain risk management frameworks, whether they deal with uncertainty, risk, or vulnerability (see e.g. Waters, 2007; Kleindorfer and Saad, 2005; Sheffi, 2005; Ritchie and Brindley, 2007; Manuj and Mentzer, 2008) aim to determine the optimal cost/benefit approach in the managing of the unwanted events. Our framework has the possibility of extending these by illustrating to managers what is in fact known (or not known) about the particular events, thus allowing more purposeful and precise use of the frameworks according to the information available. 5.3 Further research directions The illustrated framework linking theories of uncertainty to the supply chain context is novel, and therefore one goal of this study was to act as a catalyst triggering further research. Future studies could both improve and test the argument provided here. Given that the framework is conceptual, it needs to be empirically tested and validated by the academic community in further research endeavours. Such studies could use it to analyse a certain type of event with a large dataset, for instance, in which the level of uncertainty is assessed against the performance effectiveness of supply chain risk management. It would also be useful to conduct qualitative studies exploring various types of processes related to decision making under different levels of uncertainty. References Blos, M.F., Quaddus, M., Wee, H.M. and Watanabe, K. (2009), “Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil”, Supply Chain Management: An International Journal, Vol. 14 No. 4, pp. 247-252. Bogataj, D. and Bogataj, M. (2007), “Measuring the supply chain risk and vulnerability in frequency space”, International Journal of Production Economics, Vol. 108 Nos 1-2, pp. 291-301. Chopra, S. and Sodhi, M.S. (2004), “Managing risk to avoid supply-chain breakdown”, MIT Sloan Management Review, Vol. 46 No. 1, pp. 53-61. Christopher, M. and Peck, H. (2004), “Building the resilient supply chain”, International Journal of Logistics Management, Vol. 15 No. 2, pp. 1-13. Craighead, C.W., Blackhurst, J., Rungtusanatham, M.J. and Handfield, R.B. (2007), “The severity of supply chain disruptions: design characteristics and mitigation capabilities”, Decision Sciences, Vol. 38 No. 1, pp. 131-156. Davis, T. (1993), “Effective supply chain management”, Sloan Management Review, Vol. 34 No. 4, pp. 35-46. De Meyer, A., Loch, C.H. and Pich, M.T. (2002), “Managing project uncertainty: from variation to chaos”, MIT Sloan Management Review, Vol. 42 No. 2, pp. 60-67. Diekmann, J.E., Sewester, E.E. and Taher, K. (1988), Risk Management in Capital Projects, Construction Industry Institute, Austin, TX. Dosi, G. and Egidi, M. (1991), “Substantive and procedural uncertainty: an exploration of economic behaviours in changing environments”, Journal of Evolutionary Economics, Vol. 1 No. 2, pp. 145-168.
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Trkman, P. and McCormack, K. (2009), “Supply chain risk in turbulent environments-a conceptual model for managing supply chain network risk”, International Journal of Production Economics, Vol. 119 No. 2, pp. 247-258. Van der Vorst, J.G.A.J. and Beulens, A.J.M. (2002), “Identifying sources of uncertainty to generate supply chain redesign strategies”, International Journal of Physical Distribution and Logistics Management, Vol. 32 No. 6, pp. 409-430. Vilko, J.P.P. and Hallikas, J.M. (2012), “Risk assessment in multimodal supply chains”, International Journal of Production Economics, Vol. 140 No. 2, pp. 586-595. Wagner, S.M. and Neshat, N. (2010), “Assessing the vulnerability of supply chains using graph theory”, International Journal of Production Economics, Vol. 126 No. 1, pp. 121-129. Ward, S. and Chapman, C. (2003), “Transforming project risk management into project uncertainty management”, International Journal of Project Management, Vol. 21 No. 2, pp. 97-105. Waters, D. (2007), Supply Chain Risk Management: Vulnerability and Resilience in Logistics, Kogan Page Limited, London. Weick, K.E. (1993), “The collapse of sensemaking in organizations: the Mann Gulch disaster”, Administrative Science Quarterly, Vol. 38 No. 4, pp. 628-652. Wilding, R. (1998) “The supply chain complexity triangle: uncertainty generation in the supply chain”, International Journal of Physical Distribution & Logistics Management, Vol. 28 No. 8, pp. 599-616. Yang, Y.C. (2011), “Risk management of Taiwan’s maritime supply chain security”, Safety Sciences, Vol. 49 No. 3, pp. 382-393. Further reading Kyla¨heiko, K. (1998), “Making sense of technology: towards a synthesis between neoclassical and evolutionary approaches”, International Journal of Production Economics, Vols 56-57 Nos 1-3, pp. 319-332. About the authors Dr Jyri Vilko (DSc Econ and Bus Adm, MSc, Tech) is a Post-Doctoral Researcher in the School of Business at the Lappeenranta University of Technology, Finland. His recent research interests are in the areas of supply chain risk management, inter-firm relations, operations research and outsourcing. He has published on these topics in high-quality academic journals such as International Journal of Production Economics and International Journal of Shipping and Transport Logistics. He has also been involved in business practice with regard to these topics through his research, and in speaker and advisory roles. Previously he worked as a Project Manager in the project STOCA studying risk in the cargo flows of Gulf of Finland in emergency situations. Dr Jyri Vilko is the corresponding author and can be contacted at:
[email protected] Dr Paavo Ritala (DSc Econ and Bus Adm) is a Post-Doctoral Researcher in the School of Business and a Research Manager in the Technology Business Research Center at the Lappeenranta University of Technology. His recent research interests are in the areas of inter-organisational networks, business models, innovation and coopetition (collaboration between competing firms). He has published on these topics in high-quality academic journals such as Journal of Product Innovation Management, British Journal of Management & Technovation. He has also been involved in business practice with regard to these topics through his research, and in speaker and advisory roles. Dr Jan Edelmann (DSc, Econ and Bus Adm) is currently working in the private sector. His research interests include innovation management, investment decision-making and strategy. To purchase reprints of this article please e-mail:
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