The paper first gives a general overview of the drivers of uncertainty in supply chains (SC) and its consequences. To emphasize the current relevance of the topic, the uncertainties caused by the Covid-19 pandemic are ensuing explained using the SC of the food industry as an example. Additionally, the aim of the thesis is to present potential solution approaches to minimize uncertainties in order to improve the overall bottom-line performance.
First the definition and the meaning of the term uncertainty will be analysed. Secondly, a closer look will be taken on the various causes of uncertainty and the possibility to cluster them. This is followed by possible courses of action for companies in dealing with uncertain events. Next, the food logistic industry and its challenges (especially with regard to Covid) will be described. In the last chapter of the paper, a conclusion is given how all participants of SCN should handle uncertainties right now and in the future.
Table of Contents
Table of Contents
List of Figures
List of Abbreviations
1. Introduction
2. Characteristics and Analysis of Uncertainty
2.1 Definition and Meaning of Uncertainty
2.2 Sources and Classification of Uncertainty
2.3 Consequences of Uncertainties
2.4 How to Cope with Uncertainties in the Supply Chain Context
3. Uncertainties in the Food Logistics Caused by Covid-19
3.1 Food Logistics Industry
3.2 Food Supply Chain Network
3.3 Challenges in Food Logistics
3.4 Uncertainties Caused by Covid-19 and its Influences on the Food Logistics
4. Conclusion
References
List of Figures
Figure 1: Sources of Uncertainty Divided into Three Main Groups.
Figure 2: Causes and Effects of Uncertainties.
Figure 3: Network of a Food Supply Chain.
Figure 4: Cause-Effect Model Concerning Stock Outs in Retail Outlets.
List of Abbreviations
Abbildung in dieser Leseprobe nicht enthalten
1.Introduction
There are many firms which still see themselves as independent organisations which have to compete against other market players to survive. This limited view leads to distinctions and it incurs reluctance to cooperate.1 This seemingly outdated point of view leads us to the concept of supply chain management (SCM).
The overall goal of supply chains (SC) is to satisfy the needs of the customer as efficient as possible by purchasing raw materials, modifying these raw materials into finished products, and retailing these products among customers. Therefore, it is important that the company itself is not seen as a single entity but as an entity which involves many parties in a variety of stages. This is why it is more precise to use the word Supply Chain Network (SCN) to describe the structure. A typical SCN consists not only of the supplier and the manufactures but also of warehouses, retailers, transporters and customers.2
To fulfil a customer’s request as best as possible, processes3, need to be optimized in order to save costs or increase quality. Thus, in addition to the optimization of internal company processes, the coordination of cross-company collaboration along the entire SC should be considered as well, since internal company processes only offer relatively small potential for increasing efficiency. This leads to the importance of a successfully working SCN as they are dependent on one another.4 Additionally, there is not only one player involved in different stages. On the contrary, in a SCN each stage receives its products from several suppliers and sends output to several customers.5
One can recognize that a SCN is a complex system, where many of decisions have to be made to deliver the best results. Tordecilla explains that the more parties are involved “the more complex a system is, the more imprecise or inexact is the information available to characterize it and, therefore, the greater the uncertainty level.”6 And for more than sixty years, complexity has been one of the greatest challenges in business and science7, which demonstrates the importance of the paper’s topic.
Within this framework the performance of a SC can be impacted by internal and external sources, which cause various uncertainties.8 And “the more uncertainty related to a process, the more waste there will be in process”.9 Which underlines the importance of a good working SCN in order to reduce uncertainties to eliminate waste.10
Additionally, the current pandemic Covid-19 demonstrates even more the vulnerability of a SC to its unstable environment and makes clear that nothing can be planned in logistics.11 The world is characterized by uncertainties and unforeseeable events which create individual challenges along the whole SC.12 Especially the transport, the SC and the human resources of the food industry can feel the effects of the coronavirus crisis.13
Therefore, the present paper first gives a general overview of the drivers of uncertainty in a SC and its consequences. To emphasize the current relevance of the topic, the uncertainties caused by the Covid-19 pandemic are ensuing explained using the SC of the food industry as an example. Additionally, the aim of the thesis is to present potential solution approaches to minimize uncertainties in order to improve the overall bottom-line performance.
Therefore, the present paper is organized as follows. First of all, the definition and the meaning of the term uncertainty will be analysed. Secondly, a closer look will be taken on the various causes of uncertainty and the possibility to cluster them. This is followed by possible courses of action for companies in dealing with uncertain events. Next, the food logistic industry and its challenges (especially with regard to Covid) will be described. In the last chapter of the paper, a conclusion is given how all participants of SCN should handle uncertainties right now and in the future.
2.Characteristics and Analysis of Uncertainty
2.1. Definition and Meaning of Uncertainty
To get a better understanding for the term “supply chain uncertainty” (SCU) it needs to be specified. In subject-related literature, SCU is used as a synonym to “supply chain risk”.14 However, not everyone shares this opinion. Ward and Chapman explain that both terms can be clearly distinguished.15 The Oxford English Dictionary, on the one hand, points out that risk is “the possibility of harm, damage […], danger, loss […] or something unpleasant resulting”.16 This means that risk is always negatively connoted. Uncertainty, on the other hand, is a rather neutral term which refers to threats and opportunities and can therefore be interpreted positively or negatively.17
When it comes to uncertainty in a SC, it refers to situations in which decisions have to be made.
[Often,] the decision maker does not know definitely what to decide as he is indistinct about the objectives; lacks information about (or understanding of) the SC or its environment; lacks information processing capabilities; is unable to accurately predict the impact of possible control actions on SC behaviour; or, lacks effective control actions (non-controllability).18
Van der Vorst and Beulens base their definition of SCU on the five requirements of an effective management system according to de Leeuw, a Dutch organizational theorist and professor.19 From this follows that SCU is given to a decision maker if one or more of these requirements are not fulfilled:
(1) Objective: There must be a concrete and measurable goal to lead the SC to its given objectives.20
(2) Model of the Controlled System: In order to be able to manage the SC, the reaction of the system to certain actions, the involved parties and the relationship towards them, the influencing factors and measuring instruments must be known.
(3) Information on the environment and state of the system: To be able to calculate the system and possible system reactions in the future, one must know the exact environment and have knowledge about the current state of the SC.
(4) Sufficient steering measures: It must be possible to measure the SC and to record its performance on its way to reach the agreed objectives.
(5) Capacity of information processing: There need to be sufficient enough capacities in order to process information about the SC and its environment.21
However, all these are only approaches to try to define uncertainties within SC, as there cannot be found a uniform definition of this term.22 This is why Ward and Chapmann suggest to see uncertainty about anything that has an impact on the SC and define all the impacts as “simple lack[s] of uncertainty”23.
2.2. Sources and Classification of Uncertainty
The scope of uncertainty is enormous due to human actions and thus planning always involves uncertainty.24 The causes of uncertainty can basically be attributed to two factors:
(1) Objective uncertainty refers to a dynamic world that is subject to permanent change. Consequently, any planning concerning the future is connected with unknown or uncertain facts.25
(2) Subjective uncertainty is caused by the lack of human ability to fully comprehend complex systems. Therefore, in this case all influencing factors are never fully known, so that there is always a certain degree of uncertainty.26
Another approach to classify uncertainties is based on Simangunsong et al., who identified 14 sources of uncertainty throughout the SC. He assigns them to three groups (see figure 1).
Abbildung in dieser Leseprobe nicht enthalten
Figure 1: Sources of Uncertainty Divided into Three Main Groups. (Source: Own illustration based on Simangunsong, Eliot et al. (2012), pp. 4498-4499.)
The first group describes uncertainties in the investigated institution. These include product characteristics, organisational issues, decision or IT complexity. The second group identifies reasons for uncertainties that can be found within the SC, such as end-customer demand, order forecast horizon, infrastructure or facilities. The third group focuses on external uncertainties outside the service network. These include environmental factors (competitor behaviour, government regulation) or natural uncertainties (earthquake, hurricane).
However, the differentiation of the different sources is not clear, which is why the factors influence one another and also overlap.27
Nevertheless, Simangunsong et al. argue that further literature research is needed to obtain a one-time empirical result. Especially when a factor is rarely mentioned a more detailed research is needed. Many publications mention some of the 14 factors identified, but not one paper covers all of them. Various approaches can be found in the literature to subdivide the causes of uncertainty.28 As Simangunsong's classification is highly recognised in general literature, this paper refers to his approach.29
2.3. Consequences of Uncertainties
An impact-related consideration of uncertainties is oriented towards the consequences resulting from their occurrence. A fundamental differentiation can be made between operational and disruptive uncertainties based on the strength of the effects. Alternatively, the effects of the uncertainties can also be described as delaying or interrupting.30
Operational (delaying) uncertainty refers to inherent fluctuations that are part of a SC. These are, for example, deviations in production times, fluctuations in demand or transport times that deviate from planning.31
In contrast, disruptive (interrupting) uncertainties stand for events that have a massive impact on the benefit system. These include, for example, machine breakdowns and accidents in production, but also external circumstances that cannot be influenced, such as natural disasters and strikes. In subject specific literature, statements like "the probability of a default was found to be so small that it is difficult to detect"32 are common. The researcher and scientist Taleb calls such unforeseen events rare but powerful "black swans". These “black swans” as disruptive improbabilities present companies and their value chain with more serious challenges than the operational ones.33
The dangers the drivers of uncertainties pose, but also the opportunities they offer, lie in their impact on the flow of goods, information and finance. This work focuses on the observation of the flows of goods.34 Basically, an organisation's flow of goods can influence the value chain at three different levels, which can be derived from the transformation theory (input–transformation–output).35
These are the supply of goods, the processing of goods and the demand for goods (see figure 2).36
For instance, one of the biggest sources of internal SCU is fluctuations in demand. These affect the production and distribution systems of organisations and have therefore an influence on all three levels. They can lead to customer needs no longer being met and customer satisfaction declining. This, in turn, could result in a loss of market share or high storage costs - both scenarios are trying to be avoided by companies.37
[...]
1 cf. Christopher, Martin (2011), p. 13.
2 cf. Chopra, Sunil (2018), p. 15.
3 e.g. strategic planning process, procurement process, warehousing process, transportation.
4 cf. Kummer, Sebastian; Grün, Oskar; Jammernegg (2013), p. 313.
5 cf. Chopra, Sunil (2018), p. 16.
6 Tordecilla, Rafael D. et al. (2020), p.1.
7 cf. Ivanov, Dmitry (2018), p. 22.
8 cf. Bhatnagara, Rohit / Sohalb Amrik S. (2005), p.443.
9 Person, Göran (1995), p.17.
10 cf. Wee, H.M. / Wu, Simon (2009), pp. 336-337.
11 cf. Karmaker, Chitra Lekha (2021), p. 411.
12 cf. Zitzmann, Immanuel (2018), p. 57.
13 cf. Genkin, Artem S. / Mikheev, Alexey, A. (2020), p. 204.
14 cf. Ritchie, Bob / Brindley, Claire (2007), p. 307.
15 cf. Ward, Stephen / Chapmann, Chris (2001), p. 98.
16 Oxford University Press (2020).
17 cf. Ward, Stephen / Chapmann, Chris (2001), pp. 98-99.
18 Van der Vorst, Jack G. A. J. / Beulens, Adrie J. M. (2002), p. 413.
19 cf. Van der Vorst, Jack G. A. J. / Beulens, Adrie J. M. (2002), p. 413.
20 cf. De Leeuw, Antonius Cornelis Joannes (2002), p. 158.
21 cf. ibid.
22 cf. Zimmermann, Hans-Jürgen (2000), p. 191.
23 Ward, Stephen / Chapmann, Chris (2001), pp. 98.
24 cf. Voigt, Kai-Ingo (2002), pp. 485-486.
25 cf. Zitzmann, Immanuel (2018), p. 50.
26 cf. ibid.
27 cf. Simangunsong, Eliot et al. (2012), pp. 4489-4499.
28 cf. Simangunsong, Eliot et al. (2012), p. 4500.
29 cf. Zitzmann, Immanuel (2018), pp. 3,52 ; Kim, Minkyun / Chai, Sangmi (2016), p. 466; Fan, Yiyi / Stevenson, Mark (2018), pp. 205-230; Kreye, Melanie E. (2018), pp. 90-99; Brandon‐Jones, Emma, et al. (2014), pp. 55-73; Hasani, Aliakbar / Amirhossein Khosrojerdi (2016), pp. 20-52.
30 cf. Sodhi, ManMohan S. / Tang, Christopher (2012), p. 18
31 cf. Tang, Christopher / Tomlin, Brian (2008), p. 14.
32 Taleb, Nassim Nicholas (2012), p. 402.
33 cf. Taleb, Nassim Nicholas (2012), pp. 20, 101, 402.
34 cf. Zitzmann, Immanuel (2018), p. 60.
35 cf. Rafele, Carlo (2004), p. 282.
36 cf. Jüttner, Uta (2005), p. 123.
37 cf. Gupta, Anshuman / Maranas, Costas (2003), pp. 1221-1222.