Keywords

1 Key Aspects of a Truck Platooning Business Model from a Road Operator’s Perspective

Truck platooning aims to increase efficiency in terms of traffic and fuel efficiency as well as traffic safety. As such, it represents a promising means for different stakeholders to improve future transport of goods. Related work targeted at business models for truck platooning investigates business models for different stakeholders, e.g. road operators, freight forwarders/logistics operators, truck manufacturers or platoon service provider. Freight forwarder will typically benefit from (cf. [9])

  • Reduced fuel consumption and fuel costs.

  • Reduced emissions and emission-related costs.

  • Reduced requirements for drivers when platooning might lead to adaptations of maximum driving times, thus affecting personnel costs and transport times.

  • In higher automation levels, driver in following vehicles in a platoon may be allowed to rest; thus, shortening long-distance transport times may be supported.

However, gaining these benefits will depend on influence factors such as (cf. [1, 9, 11])

  • Gain sharing model between trucks in a platoon.

  • Pricing model of platoon service providers.

  • Percentage of highway kilometres eligible for platooning and amount of viable platooning time.

  • Penetration of trucks with platooning technology (SAE L1-L5).

  • Ability to form multi-fleet, multi-brand platoons.

  • National and European legal regulation for truck drivers’ resting times or minimum distance between trucks in a platoon.

  • Internalisation of external costs (e.g. air pollution) for logistics operators.

Platoon service providers (PSP) are considered to play a crucial role when it comes to the deployment of truck platooning. Central activities of PSPs are the platoon management and the financial clearing between platoon members. Examples for business model elements of truck platooning have already been published by [1, 5, 9, 11, 14]. In Connecting Austria, the support of truck platooning via C-ITS has been a vital element. Lu et al. (cf. [12]) consider C-ITS applications as important means to increase safety, efficiency and sustainability of road transport. They conducted a survey and revealed that respondents consider C-ITS services as important and relevant. However, the study also indicated a low willingness to pay, which demands for sustainable business models. Since C-ITS is an important issue for safe and efficient truck platooning and related work does not discuss business models in the C-ITS context, in addition to previous related work a business model canvas (cf. [15]) for truck platooning has been developed from the point of view of a road operator.

Fig. 14.1
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www.strategyzer.com adapted from [15]. Courtesy of Strategyzer AG

Business model canvas for truck platooning from a road operator’s point of view.

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The result is depicted in Fig. 14.1. The results gained in Connecting Austria are based on related work and interviews/workshops, which are referenced using (CA) in Fig. 14.1 . In general, participants of empirical studies conducted in Connecting Austria provided an informed consent to participate and to publish the results. Furthermore, interview data was anonymised and securely stored internally in the University of Applied Sciences Upper Austria. In advance to the empirical studies, the authorisation of the involved parties related to the interviews was provided.

Based on the gathered data, a value proposition canvas (cf. [16]) has been developed for two customer segments. The value proposition canvas especially details the elements “value proposition” and “customer segment” of the business model canvas. The two customer segments investigated in detail are (1) the “government” as owner of public road operators (compare Fig. 14.2) and (2) the “platoon service providers”. Since the information (e.g. dynamic information on platooning-eligible highway segments) provided by the road operator is highly relevant for platoon service providers when planning and managing platoons, a value proposition canvas for the customer segment “platoon service provider" has been developed (compare Fig. 14.3).

Fig. 14.2
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Value proposition canvas for the customer segment government. www.strategyzer.com adapted from [16]. Courtesy of Strategyzer AG

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Fig. 14.3
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Value proposition canvas for the customer segment platoon service provider. www.strategyzer.com adapted from [16]. Courtesy of Strategyzer AG

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The results of analysing both value proposition canvas indicate:

  • Commonalities within the customer segments.

    • With regard to pains while doing their job(s), both segments suffer from increased road traffic, traffic jams and the respective costs as well as accidents on the motorway and their negative implications.

    • Gains for both segments in doing their jobs are to reduce emissions and increase traffic safety due to supporting platooning technology.

  • Differences within the customer segments.

    • Customer Job(s): While the platooning service provider focuses on the multi-brand–multi-fleet platoon organisation either pretrip to be included into logistics organisation or on-trip ad hoc platooning, the government defines its jobs as providing high-quality road infrastructure for efficient and safe people and goods transport and complies with national and European goals for sustainable transport.

    • With regard to pains while doing their job(s), the differences are: for the platoon service provider, the missing information on planned routes of logistics operators (including details on loading or technical status of the trucks involved) and the low number of trucks able to platoon are the main pain points. For the government, the main pain point is the negative impact of increasing freight transport on sustainability goals (e.g. CO\(_2\) emissions)

    • The gains in the customer segments differ as follows: the platooning service provider mainly focuses on the increasing number of kilometres driven in a platoon due to optimal planning and the generation of revenues in case of successful organisation of platoons. In comparison, the main gains for the government are to support Austria as excellent business location with its road network, foster the leading role in European road operators’ community due to high innovation rate and enlarge the leading role in C-ITS technology deployment.

  • Commonalities within the value proposition.

    • Products and services are the same for both customer segments.

      • C-ITS stations.

      • Dynamic risk-rated map.

      • Traffic management centre able to support and use C-ITS.

    • With regard to gain creators, for both customer segments the first common issue is providing C-ITS messages at Austrian motorways to support platooning, while the second common gain creator is the provision of a dynamic risk-rated map.

    • The main commonalities with respect to pain relievers are

      • Dynamic traffic management with C-ITS using hybrid communication technology approaches to provide reliable and secure recommendations for platoons to increase efficiency.

      • Increase traffic safety via supporting truck platooning.

      • Reduction of CO\(_2\) emissions by supporting truck platooning with dynamic C-ITS traffic management.

  • Differences within the value proposition

    • With regard to gain creators, there are two differences for the customer segments: only for the customer segment “government”, the implementation of C-ITS stations as well as the C-ITS impact assessment is important.

    • One difference regarding pain relievers mentioned is the support of the market take-up of platooning due to positive image based on increased sustainability. Road operators proactive cooperation with truck manufacturers can support this issue and help the customer segment “platooning service provider”.

So far, roadmaps for the deployment of truck platooning systems were presented by different institutions (cf. [5, 13]). Typically, the introduction of automated driving assistance function is structured along the automation levels (SAE L1-L4) and a time horizon. The deployment is considered to be evolutionary, i.e. incremental from one level to the next. To monitor and foster truck platooning deployment, not only vehicle innovations need to be investigated, but also innovations related to intelligent road infrastructures as well as the innovation readiness of logistics operators. For this reason, continuous trend monitoring is crucial to be prepared for future trends and to be able to adjust business models accordingly. Within the next section, trend monitoring as one main key feature for business model development and innovation is explained and applied in the context of logistics and automated driving.

2 Trend Monitoring as a Key Feature for Business Model Development and Innovation

2.1 Relevance of Trend Monitoring for Business Model Development

Markets, value networks and the organisational environment are changing faster than ever before. Once stable market mechanisms and predictable developments have become more and more instable and complex, developments and trends can hardly be predicted. With emerging topics and technologies such as artificial intelligence, cyber-physical systems, robotics or augmented and mixed reality, organisations have to deal with an increasing amount of strategic uncertainty. The ability to identify relevant trends and developments and to analyse their implications on current businesses becomes a crucial capability. Same applies to deriving opportunities for innovating existing and for generating new business models based on relevant trends (cf. [10]). Once reliable and proven business models may need to be innovated or completely rethought. Systematic and structured trend monitoring provides an approach of how to identify, analyse and continuously re-evaluate relevant trends and developments in the organisational environment. The outputs and findings of trend monitoring represent an important knowledge base for evaluating the applicability and future-fit of existing business models and for deriving potential business model innovation opportunities (cf. [6]). Existing literature shows that organisations with established trend monitoring initiatives can observe and track a larger amount of strategically relevant issues than those without such systems. Trend monitoring activities should not focus on external sources only, but also allow for integrating internal information, sources, networks and structures as well (cf. [2]). A comparison of practices and literature on trend monitoring shows that there are basically three steps involved in trend monitoring activities: (i) definition of search fields, (ii) identification and collection of inputs and trends and (iii) evaluation of trends and deduction of strategic implications (cf. [3]). In step 1, strategic innovation search fields provide the basis for all following activities and lay the foundation for trend identification and input collection (cf. [8]). Within defined search fields, input can systematically be collected and trends can be identified in step 2. Finally, the collected and clearly described trends and developments can be used as the basis for trend evaluation in the third step (Brandtner, 2018). Typical evaluation criteria may include the maturity or the probability of a certain trend and the estimated impact of the trend on the organisation’s businesses (cf. [17]).

2.2 Applying Trend Monitoring in the Context of Logistics and Automated Driving

Based on the three-step logic of trend monitoring discussed in the previous section, a model for applying trend monitoring in the context of logistics and automated driving was developed. Fig. 14.4 depicts the developed approach:

Fig. 14.4
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Developed trend monitoring approach for logistics and automated driving

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In step one, a group of researchers and professors in the area of transport logistics, automated driving and strategic foresight respectively trend monitoring defined search terms. This approach ensured the consideration of a complete and domain-based set of keywords and search fields for the subsequent stages of the procedure. Step 2—identification and collection of inputs and trends—was headed by a research associate with expertise in trend monitoring and applying the software TRENDONE. In the third step—evaluation of trends and deduction of strategic implications—the prepared trend profiles were sent out for evaluation to domain experts. In total, 12 experts from the sectors of smart mobility, logistics and transport, research and academia, technology development, public domain, software development and R&D evaluated the trend profiles. All the experts provided an informed consent to participate in the study and to publish the results. Based on the expert evaluations, an aggregated view of the trend profiles was generated and visualised in the form of a trend radar (compare Fig. 14.5).

2.3 Implications for Business Model Development Related to Logistics and Automated Driving

The results of the trend evaluation and the depiction of these trends on the developed trend radar showed that:

  • 5 macro-trends were categorised as ACT trends.

  • 11 macro-trends fell into the PREPARE category.

  • 2 of the selected macro-trends were WATCH trends.

  • None of the selected macro-trends were evaluated as OUT OF SCOPE trend, indicating that the search fields were defined in a focused and matching way.

Fig. 14.5
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Trend radar for logistics and automated driving

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The final trend radar (compare Fig. 14.5) looks as follows and represents the main outcome of the conducted trend monitoring approach. ACT trends have a high potential of influencing logistics and automated driving already now or at least in the very near future. They show a high degree of maturity and are tangible and applicable in the given context. In the defined field of logistics and automated driving, the participants assigned five trends to the ACT category (cf. Table 14.1). For these ACT trends, the recommendation for action is to integrate them into the current innovation portfolio and start defining concrete actions in these areas. PREPARE trends have a high potential of influencing logistics and automated driving. However, their maturity is not as far progressed as it is the case for ACT trends. Hence, organisations may still have some time to deal with these trends and prepare a strategy on how they get further information. In the given context, 11 trends were identified as PREPARE trends (cf. Table 14.1). Finally, WATCH trends are those trends that currently show little potential of influencing automated driving within the logistics domain but have a high degree of maturity. These trends should be watched; i.e. organisation should continuously monitor the developments of these trends also in other industries in order not to miss relevant changes. In the given context, 2 trends were evaluated as WATCH trends (cf. Table 14.1).

Table 14.1 Evaluation results of trend monitoring for automated driving within the logistics domain
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3 Discussion and Conclusion

A main barrier for innovation and the related investment may represent a technological lock-in effect (cf. [7]). High investment cost in C-ITS infrastructure for road operators has been such a barrier the last years. On a European level, the C-ITS Deployment Platform was set up in 2014 to support a harmonised deployment across European countries (cf. [12]). With ASFINAG’s roll-out of C-ITS stations at 2000 highway kilometres in November 2020, the C-ITS deployment started in Austria and the foundation for Day-1 and Day-2 C-ITS services like cooperative platooning was established. Even if the initial investment in Austria took already place, viable business models for the operation, maintenance and the enhancement of C-ITS services are still an open issue. Examples in existing publications propose as revenue streams taxes/tolls, reduced road maintenance costs or earnings from data provision services. However, when developing/deploying new C-ITS services viable business models should be investigated to support road operators and technology providers in taking informed decisions and sustainably deploying C-ITS services.

In addition to the C-ITS business model investigation, the relevance of ongoing trend monitoring to continuously adapting business models was discussed in this contribution. Doing so, a trend monitor related to logistics and automated driving was set up and rated by members of the Connecting Austria consortium. The members identified the following 5 ACT trends—emission reduction, last mile solutions, truck platooning, harmonisation of legislation and augmented and mixed reality. Emission reduction gained momentum in the last decades (cf. European Green Deal) and forces car manufacturers to rethink traditional propulsion systems, logistics operators to optimise transport processes and evaluate solutions for sustainable logistics operations, and road operators to increase traffic efficiency and enforce access regulations (e.g. Urban Access Regulations for Low Emissions Zones). Emission reduction is also crucial in urban areas and demands for innovative and sustainable last mile solutions (delivery robots, pickup stations, etc.). However, also long-distance transport between urban areas needs to be optimised. Truck platooning as such represents a means to increase efficiency and safety. A major prerequisite for truck platooning and automated driving is a harmonised legislation. Especially, long-distance transport across borders with truck platoons requires a harmonisation of legislation in order to support efficient operation and trigger platooning technology providers to offer products. As fifth trend, augmented and mixed reality has been identified as ACT trend. Augmented reality may support truck drivers via displaying relevant driving information (e.g. navigation information, display of C-ITS information). Overall, 11 PREPARE trends have been identified. These trends may be allocated to the following three clusters—automated driving technologies, clean tech and logistics trends. Automated vehicles (SAE L3-L5) are currently under development. Artificial intelligence algorithms (deep learning) and communication technologies such as 5G and ITS-G5 fuel automated driving functions. Furthermore, information provided from the road infrastructure related to the available infrastructure support for automated driving functions (cf. ISAD levels [4]) will be relevant to implement highly automated driving in a safe manner. Logistics trends will adopt new technologies and aim to apply them in order to meet sustainability goals. Furthermore, logistics operators will need to rethink current job profiles especially when it comes to highly automated driving. Clean tech, as third cluster, comprises renewable energy technologies, technologies to reduce emissions, safe energy and resource exploitation.

Finally, two trends—traffic simulation and cooperative intelligent transport systems (C-ITS)—were assigned to the WATCH category. This category typically comprises trends that have a high maturity. However, the trend potential is currently considered to be low or not yet adopted within a given domain. Regarding the judgements of the trend monitor participants, the potential of C-ITS and traffic simulation for automated driving within the logistics domain needs to be monitored, assessed and adopted in future.