The future of logistics and supply chain management and how gamification can help to get prepare it
Prof. Susanne Wilpers, Herbert Streit September 2020
Logistics and supply chain management are areas of work that are subject to rapid change. The clear tendency in the past was continuous growth adopting new trends and technologies from industrial and agricultural production. This couldn’t even be stopped by dramatic political and economic events; only be delayed for some time. The same can be expected for the actual SARS COVID 19 pandemic. Consequently, the training of logistics experts must always be adapted to the latest trends. For this purpose, the teaching of theoretical principles is an indispensable factor. However, it also makes sense to con-sider further learning opportunities, especially those with a practical orientation. A gamification approach would be a promising tool to achieve this.
Such a game provides an opportunity to apply the theoretical principles in a practical simulation. This applies to the basic work as well as to situations where the influencing factors have changed, be it through evolutionary changes in supply and demand, or through challenging situations such as a natural disaster or a pandemic.
In this article, such a gaming approach is presented. Internal and external factors that have influenced the character of logistics and supply chain management in the past are analyzed and evaluated. Different forecast possibilities that could describe the future of logistics are compared and their suitability for a gaming approach is rated.
Finally, the structure and functionality of such a game will be described. A basic requirement is the availability of all relevant data in a continuously updated context. These data are processed in the analysis area of the game using high-quality analysis tools, so that the player can finally use the most realistic scenario with the corresponding set of parameters.
The goal of every game is to keep the incoming and outgoing logistics of your company or institution alive with the available tools and at the lowest financial cost.
This would enable our future logistics experts to be trained for future changes and especially for future crises in a way that comes very close to reality.
With such a highly sophisticated game and regularly updated real data, companies and institutions could also prepare their internal and external logistics and supply chains to mitigate the risks that future disruptions will bring.
Keywords: Logistics, Supply Chain Management, transportation, game, future, disruption logistics training, students’ training
1 Introduction – 1 –
2 What factors have changed logistics in the past and might change it in the future? – 2 –
2.1 Internal factors that have caused changes to logistics – 2 –
2.1.1 Cost – 2 –
2.1.2 Quality – 2 –
2.1.3 Customers – 3 –
2.1.4 Regulations – 3 –
2.1.5 Resources – 4 –
2.2 External factors that will most likely change logistics in the future – 4 –
2.2.1 Digitalization and Big Data – 4 –
2.2.2 Growing population and growing economy – 4 –
2.3 External disruptive factors – 5 –
3 Predictive models – 6 –
3.1 Naive forecast – 6 –
3.2 Qualitative forecast – 6 –
3.3 Quantitative forecast – 7 –
4 Reflecting the different future models of logistics and supply chain management in a gamification approach – 7 –
4.1 How to implement – 7 –
4.2 Anatomy of the game – 8 –
5 Conclusion – 9 –
List of figures:
The worldwide transport volumes and the storage capacities of goods are expected to triple until 2050 (ITF, 2019). Can such a prediction be taken seriously? Will the SARS COVID pandemic of 2020 cause a sensitive set-back to the world’s economy in the long term and in consequence have a negative effect on logistics? How reliable are predictions and forecasts at all?
What can we do to get our future experts in logistics and supply chain management prepared for the whole bandwidth of future scenarios?
This paper will take a look at the methods of forecasting, the factors that influenced the growth and changes in logistics and supply chain management in the past, in the present and most likely in the future. Furthermore, it gives an overview of the common prediction methods and discusses their reliability.
To prepare our forthcoming logistics experts accordingly, we need more than just a theory-based academic training, although this will still be an essential part of their education. A gamification approach could be a promising instrument to prepare them for the whole bandwidth of forthcoming situations in a game-like scenario, where they learn to weather the challenges and obstacles in their future employment.
We will discuss the prerequisites and the structure of such a game, what it needs to picture possible future scenarios that are able to react accordingly to the gamers’ input. Such gaming environments would be capable to not only train students and deepen their expertise but also give them a good amount of experience on how to handle future challenges in the best possible manner.
This paper focuses on logistics (transport and storage) of goods. Transport of passengers, which is also logistics in a broader sense, is left out.
It aims for future and current logistics experts working in all kinds of organizations. From logistics and transport companies to the logistics department of companies or public institutions.
2. What factors have changed logistics in the past and might change it in the future?
Looking at the factors that have changed the way logistics – storage and transportation – worked in the past, we can identify three major factors.
Internal factors that drive changes in production, in industry and agriculture and consequently change the demands for their adherent logistics.
External factors are changes that are not directly related to production but might have a big impact on logistics.
External disruptive factors, which are global crises that affect everything around us, including production and logistics. Although they happen on a regular basis, their occurrence and impact can hardly be estimated in advance.
2.1 Internal factors that have caused changes to logistics
According to the “ITF Transport outlook 2019”, there are five internal factors in industry and agricultural production that drove changes in logistics and supply chain management in the past and will most likely do so in the future. These five factors usually work in combination with each other in varying proportions and can be used for prognostics to a certain extent, especially if there is usable data, like time series, available.
New technologies can make established ones uncompetitive due to lower costs. When railways and steamships were used to transport goods, horse- and oxen-drawn carriages were not competitive anymore for longer transport routes. Autonomous driving could have the same effect in the future, a process that has already started (Cerasis, 2020). But it might as well be that production is moved to low wage countries. It might then as well be that “old” technologies still can beat the new ones and, in this case, the demand for transportation would grow.
New technologies and/or processes raise the quality of products or services to a level that makes the old ones uncompetitive. This especially applies to Hi-Tec products and ser-vices that are essential in most countries’ industry production. In the past only a few countries were able to provide top-class technology and exported their products. With more countries now able to achieve this, the transportation volume might just as well shrink in the future.
Significant changes in consumer or business customers’ preferences make previous products or services unattractive compared to new ones
New laws or regulations no longer permit old ways of working. Labor protection rules and environment protection policies have influenced industrial and agricultural production significantly in the past. Production and manufacturing were moved to other regions or even other countries extending the supply chain and raising the transport volumes.
Regulations for decarbonization of freight transport will surely be an essential factor in the future. Today there is no doubt left about the causal link between carbon oxide emission and global warming. There is a high probability that new policies will have a big in-fluence on the future of transportation and in consequence on the future of logistics and supply chain management. The current situation is that transport (freight and passengers) is highly dependent on fossil fuels that make up more than 92% (ITF, 2019). Freight transport accounts for more than 7% of the global CO2 emission (ITF, 2015). With growing freight volume and longer supply chains, the CO2 emission from freight transport will surely grow in the next decades and would seriously undermine climate goals.
Regulations to mitigate these emissions and new technologies were offset by the growth of the emissions caused by transportations and so it can be expected that stricter regulations will take place, especially to reduce road transport that produces 53% of the transportation emissions and is still growing (ITF, 2015).
Previously important resources are no longer readily available or previously inexistent or inaccessible resources now become available (ITF, 2019). New resources are usually found in more remote areas of the globe with a longer distance for transportation.
2.2 External factors that will most likely change logistics in the future
2.2.1 Digitalization and Big Data
As more and more industries are driving their digitization to a higher level, their logistics and supply chain services will consequently have to follow this trend. With the so-called fourth industrial revolution (Industry 4.0), a huge amount of data will be available that can be used to improve volume and quality of production and transportation processes and help to achieve better quality, resilience and to reduce costs (Moore, 2019).
2.2.2 Growing population and growing economy
The relationship between population growth and economic development has been dis-cussed since at least 1798 when Thomas Malthus stated that population growth would depress living standards in the long run (Malthus, 1789). Since then this has been debated between demographers and economists and there is no clear answer at the moment.
But nevertheless, it can be stated that the global middle class is expected to grow and reach 5.5 billion by 2030 (European Commission, 2020). This part of the population with higher financial power will most likely boost up private consumption with an increase in production and worldwide transport. But as Kenneth Award Boulding said: “Anyone who believes that exponential growth can go on forever in a finite world is either a madman or an economist (The Economist, 2015).
The limit of growth might as well be another external factor to change the future of logistics (Meadows et al, 2004).
2.3 External disruptive factors
Unlike the internal factors, external factors with a large impact on the future of logistics and supply chain management are difficult to predict. In the past, this has been global or large local military conflicts with a deep impact on the world’s economy, a financial crisis like the Great Depression in the 1930s or natural disasters like volcanic eruptions. The eruption of the Icelandic volcano Eyjafjallajökull in 2010, grounded all air traffic over Europe for weeks (CBS News, 2010). Worldwide large pandemics caused by influenza occurred three times in the last century. The new coronavirus had a deep impact on the world’s economy, which is now much more globalized and dependent on international supply chains than before.
The risks and consequences of such external disruptive factors are hard to quantify using forecast models. As a result, many companies and governmental institutions do not adequately prepare for them. This said, there is an urgent need to train our present and future experts and decision-makers for such a scenario.
The good news is that after each of these dramatic events, economic growth picked up speed again within a few years and subsequently the demand for transportation of goods increased again.
Even the economic slump of the SARS COVID 19 pandemic will not change this in the long term. The first signs of economic growth can be already seen in countries where the number of infected people is low.
Figure1 shows an example of the impact of the 2008/2009 global financial crisis on the transport volume of the EU.
Figure 1: Freight transport volume and modal split within the EU
3. Predictive models
“Prediction is very difficult, especially about the future.” Mark Twain.
Predictions are useless without the evaluation of the prediction model used. In our case, there are internal and external factors that are – to a certain degree – accountable. The external disruptive factors can hardly be taken into account when planning the future of logistics and supply chain management.
To fill this gap, we should think about emergency strategies to be kept in a place that could mitigate their impact. The disruption of the supply chains during the SARS COVID 19 pandemic is a good example of what we missed in the past and should do in the future.
Before we describe a possible gaming approach to achieve such emergency preparation, we will briefly discuss different forecasting models that should be a part of our gaming scenario.
3.1 Naive forecast
The simplest form of forecasting is the so-called naive forecast. It is used to extrapolate the available present and past values into the future without much effort. It is a very simple method and can also be used to assess the quality of other, more complex forecasts by comparing the mean errors of both forecast types.
It is used more often than authors would admit. If we see that the global freight has tripled within the last 30 years, we would expect that it will as well triple within the next 30 years. The naive forecast is suitable for time series, but does not take any of the internal or external factors into account (Hyndman and Athanasopoulos, 2018). Nevertheless, it can be a useful first tool for a future scenario.
3.2 Qualitative forecast
Qualitative forecasts are subjective assessments that are intuitively made by experts with the appropriate knowledge. The data is also processed for such a forecast and concrete, numerical results are produced.
Forecasts on economic growth, for example, are qualitative. The best-known method is the Delphi method, a reiterating, multi-phase survey of experts (Linstone and Turoff, 1975). However, this type of prediction is highly error-prone due to the cognitive biases in human thinking.
Another common qualitative forecasting technique is the scenario method, where the possible best case-, the worst case- and trend-scenario are considered (Mariton, 2020).
For our gaming approach, qualitative forecasting is a valuable option for scenarios that lack a part of historic data or scenarios that cannot easily be described with data. Like in the naive forecast, prediction errors can only be assessed in retrospective, i.e. when the predicted event has occurred.
3.3 Quantitative forecast
This type of forecast is pure mathematics: data and calculation. One-dimensional fore-casting methods, such as exponential smoothing or trend forecasting, are more likely to be used for short- and medium-term forecasts because they are too imprecise for long-term forecasts.
Multidimensional forecasting methods, such as regression analysis and econometric models, also take into account causal relationships between two or more variables. With methods for determining the forecast errors (e.g. mean square error), forecast accuracy can be determined for all quantitative methods (Swamidass, 2000).
Due to this, these forecast methods would be a valuable asset in our gaming scenario. As long as the appropriate database is provided, the scenarios with the most probable likelihood could be chosen.
4. Reflecting the different future models of logistics and supply chain management in a gamification approach
4.1 How to implement
Different forecast models about the future of logistics and supply chain management can be implemented as a distinct scenario into a game to train logistics students. Each scenario then would have their particularly pronounced internal and external factors that affect or even damage the supply chains.
Different scenarios of the future of logistics and supply chains are driven by a different set of internal and external factors. In a computer game, we would call them “worlds” and the players would have the choice which one of them to choose.
The players can then demonstrate in a simulated environment what they have learned in theory. They have to find solutions, probably fail the first few times, but the goal is to try until the least expensive and disruptive solution is found.
This could be an efficient way to prepare them for their forthcoming challenges.
The goal is not to address the cause of the disruption, but to find out where the weak points and potential failures in the supply chain might happen and what the financial damage would be. The player is able to test different mitigation activities to prevent or at least minimize the damage.
4.2 Anatomy of the game
The game would consist of three layers. The database layer, the analytics layer and the user interface. Whether using a virtual logistics company to educate students or doing a business simulation in a logistics company, the prerequisite for a motivating and insightful game scenario is the availability of the appropriate data. This includes all possible data and metadata, structured or unstructured. This concept is known in industry and politics as Big Data (Gartner, 2020). So what we need is nothing less than all the data from the whole supply chain in digital form.
Such a data repository is not just key for a game, but also very useful for other business solutions of a company or institution. In a students’ and trainees’ gaming environment, this would be artificially generated or anonymized data. In a real company or institution, these data would be collected and could be stored in a cloud. In contrast to the students’ training environment, this data would have to be updated in a timely manner.
The second layer in our game structure would be the analytics layer, a Business Intelligence approach (Dedic and Stanier, 2016) This is also known as Business Analytics (Beller and Barnett, 2009) or Data Mining (Holton, 2010). These are different terms for similar procedures. It’s all about making sense of the data that are produced during every business activity. To recognize patterns, improve quality and return on investment and get support for business decisions.
In a gamification approach, this layer could work with the most sophisticated methods that are actually available: decision support, what-if analysis, artificial intelligence, machine learning and risk management.
To create this layer is a task for data scientists, not for logistics students or experts – unless they are trained in both. The analytics layer is the place where all the linkages and calculations take place. This layer is fed by the data repository.
The user-level presents the gaming interface to the (end)users: students, trainees or experts in logistics. It’s a graphical interface where the gamers can train how to do their daily job in logistics and supply chain management. In an advanced state, they finally have the opportunity to master possible future scenarios as mentioned before, when they are facing different transportation volumes, disrupted supply chains, locked down factories, closed borders, etc.
The goal is to master the challenge with the least damage for the ones they are responsible for (company, institution, population, etc.).
As we cannot look into the future, the only way to get prepared for it is to take a look at history and to forecast the most likely versions of such a future. In logistics and supply chain management it’s mostly about changing transport and storage volumes and the distances the goods have to travel. Additionally, there is also a tight corset of cost-pressure, laws and regulations that decide about success or failure.
We have seen different forecast methods that can be applied to get an idea into which direction the way will go. If we know the scenarios, we can prepare ourselves for future changes.
The best crisis managers are the ones with the appropriate experiences, who have already worked through different kinds of crises. A gamification approach could get the trainees at least halfway there, like flight simulator training for pilots.
But it stands and falls with the quality of the game and this is for a great part determined by the quality and the completeness of the data provided.
With such a highly sophisticated game and regularly updated real data, companies and institutions could also prepare their internal and external logistics and supply chains to mitigate the risks that future disruptions will bring them.
Beller, M. J.; Barnett, A. (2009): “Next Generation Business Analytics, Lightship Partners LLC (Publisher)
CBS News (2010): Iceland Volcano Spewing Ash Chokes Europe Air Travel, online: https://www.cbsnews.com/news/iceland-volcano-ash-upends-air-travel-in-europe/, processed on: 10.09.2020
Cerasis (2020): Autonomous Vehicles in Logistics: What are the Impacts?, online: https://cerasis.com/autonomous-vehicles-in-logistics/, processed on: 01.09.2020
Dedic, N., Stanier, C. (2016): Measuring the Success of Changes to Existing Business Intelligence Solutions to Improve Business Intelligence Reporting, Springer International (Publisher), Vol. 268
European Commission (2020): Developments and Forecasts of Growing Consumerism, online: https://ec.europa.eu/knowledge4policy/foresight/topic/growing-consumerism/more-developments-relevant-growing-consumerism_en, processed on: 04.09.2020
European Environment Agency (2019): Passenger and freight transport demand in Europe 2019, online: https://www.eea.europa.eu/data-and-maps/indicators/passenger-and-freight-transport-demand/assessment-1, processed on: 10.09.2020
Gartner IT Glossary (2020): Big data definition, online: http://www.gartner.com/it-glossary/big-data, processed on: 12.09.2020
Holton, L. (2010): Big Data: Mining for Nuggets of Information, online: https://www.britannica.com/topic/Big-Data-Mining-for-Nuggets-of-Information-1957644, processed on: 13.09.2020
Hyndman Rob., J.; Athanasopoulos, George. (2018): Forecasting: Principles and practice, online: https://otexts.com/fpp2/, processed on: 11.09.2020
ITF (2015): The Carbon Footprint of Global Trade, Paris, online: https://www.itf-oecd.org/sites/default/files/docs/cop-pdf-06.pdf, processed on: 02.09.2020
ITF (2019): ITF Transport Outlook 2019, OECD Publishing, Paris, online: https://www.oecd-ilibrary.org/docserver/transp_outlook-en-2019en.pdf?expires=1600427845&id=id&accname=guest&checksum=DAF274A1A8BA9BFA60CED4E0BF0DFC46, processed on: 01.09.2020
Linstone, Harold, A.; Turoff, Murray (1975): The Delphi Method: Techniques and Applications, online: https://www.researchgate.net/publication/237035943_The_Delphi_Method_Techniques_and_Applications, processed on: 11.09.2020
Malthus, Thomas (1798): An Essay on the Principle of Population, London, online: http://www.esp.org/books/malthus/population/malthus.pdf, processed on: 03.09.2020
Mariton, Jeremie (2020): What is Scenario Planning and How to Use It, online: https://www.smestrategy.net/blog/what-is-scenario-planning-and-how-to-use-it accessed, processed on: 11.09.2020
Meadows, D; Meadows, D.; Randers, J. (2004): The Limits to Growth: The 30-Year Update, Oslo, Publisher: Chelsea green publishing company
Moore, Mike (2019): What is Industry 4.0? Everything you need to know, online: https://www.techradar.com/news/what-is-industry-40-everything-you-need-to-know., processed on: 03.09.2020
Swamidass, P., M. (2000): Forecasting models: Quantitative, Boston, Publisher: Springer Verlag
The Economist (2015): Slower growth – disaster or blessing? A debate, online: http://worldif.economist.com/article/12121/debate., processed on: 09.09.2020
 European Environment Agency: Passenger and freight transport demand in Europe 2019, online
The article is part of the development of a study on the application of the game approach in logistics and transport training (Output title O4) under the Erasmus+ strategic partnership project “Building an innovative network for sharing of the best educational practices, incl. game approach, in the area of international logistic and transport”, Project number: KA203/HE25/13.09.2019