Creating collaborative Ecosystems for the Connected Truck

SERVICE AND MAINTENANCE OPERATIONS IN A DIGITAL WORLD

Abstract
The trend towards digitalization and connectivity enables truck manufacturers to create exciting new services for their customers. However, it requires a complete transformation of their business approach. We will explore the new collaborative economy in the context of autonomous trucks and the new position of commercial vehicle manufacturers. Using service and maintenance perations as a case, we will describe the participants and requirements for new collaborative ecosystems. We will describe potential technical solutions as well as the challenges and opportunities in their implementation.

1. Introduction

The trend towards digitalization, autonomous driving and connectivity enables truck manufacturers to create exciting new services for their customers. However, it requires a complete transformation of their approach towards engineering, manufacturing and service operations. Their current core capabilities must be augmented with a new understanding of technology and business processes.

Today, commercial trucks are large investments that are measured using metrics such as Total Cost of Ownership (TCO), together with qualitative considerations such as comfort, image reputation, reliability and technology. The transition towards autonomous driving and electrification will increase the competition focus even more on quantitative aspects and will force commercial vehicle manufacturers (CVMs) to expand their service offerings to provide more value to their customers. Digitalization of business processes offers such expansion possibilities, but brings also new players from unexpected directions, such as large Information Technologies companies looking for yet another market to disrupt. Hence, the overarching question for CVMs in the future is the transformation from manufacturing physical products into providing a larger slice of the value for the end customer by occupying a new position in the market.

2. Business Ecosystem for Automotive Trucks

The “job-to-be-done” from logistics & transportation is mostly fixed: Deliver the correct amount of goods to the correct place at the correct time at the lowest possible cost. This will involve the complex coordination of people, facilities and supplies. However, several mega-trends such as digitalization, connectivity and autonomous driving are changing how these goals are achieved.

Digitalization is allowing more and more data to be captured at discreet points in the logistic chain, connectivity is allowing the data to be continuously and instantly available and autonomous driving will eliminate many traditional limitations that currently inhibit commercial logistics.

The diagram above shows a simplified representation of the future business ecosystem for autonomous trucks and the current position of the Commercial Vehicle OEM related to the core value proposition.

Transportation suppliers have a pressure to provide efficient transportation at ever decreasing costs. Hence, removing the human factor provides an attractive proposition taking into consideration the current shortage of qualified drivers, the potential savings on salaries and the increased utilization of trucks. Transportation suppliers are also increasingly offering additional products and services to complement their core business, such as supply chain management or e-commerce support.

The Commercial Vehicle OEM finds themselves in a dire situation: competition on quantitative factors such as TCO is increasing, reducing potential differentiation factors based on perceived quality or reliability. Electrified power trains and increased knowledge from Tier-I suppliers delivering subsystems reduce barriers to entry for commercial vehicle construction. With a traditional ICT commercial vehicle, the power train has been part of the core value of the OEM. The ability for the vehicle to drive millions of kilometers, for example. However, this key value has been lost to the Tier-I subsystem suppliers who now supply “bolton” power train solutions. On top of this an EV power train, albeit not without challenges, is a mechanically simpler solution, now that energy storage techniques have reached a practical level. This requires the search for new business models and technological innovations to remain competitive. Potential areas of expansion include the creation of digital platforms for telematics, investment in developing technologies for autonomous driving and full service concepts.

3. Example – Collaborative Ecosystem for Maintenance and Repair

A collaborative ecosystem allows people to work together from different departments and across companies to provide services that generate value for the users. This dynamic system allows for a free flow of ideas that span the extended enterprise into all aspects of the collaborative economy as described in the previous section.

Service and maintenance operations offer a good example in the context of Commercial Vehicles. These activities are key to provide a lower TCO as they ensure a reliable vehicle with a high uptime and a predictable amount of downtime for repair and maintenance. In turn, a more comprehensive understanding of the vehicle’s life cycle can lead to a higher residual value at resale time. We will use the repair process as an example to illustrate the interaction between the players, the data exchanged and the challenges and opportunities for the implementation.

The following diagram provides a categorization of the information that is required to optimize service operations. The structured spectrum is machine readable and it can be directly used to generate statistical analysis. Unstructured information is generated either from direct communication between humans (e.g. as part of a joint remote diagnostics session) or by indirectly mapping the thought processes for problem solving. Real time data is generated dynamically from the vehicle or the repair process and can be monitored or analysed immediately, while asynchronous data is received with a potential large delay after the trigger event.

These two spectrums allow us to categorize the information. As an example, a Vehicle Health and System State containing issues detected by the vehicle electronics can be provided in a structured way in real time to a cloud system by using telematics systems with advanced diagnostics capabilities, while forums and free-form customer feedback can be interpreted with Natural Language Processing to process the data.

The analysis of the generated information requires the cooperation of many participants that can be categorized in four main groups as seen in the following diagram. Items on the border of the circle have today a loose connection to organization.

Once we know some typical information sources and the actors that take place to deliver solutions for the end customer, we can look at a continuous product improvement cycle as an example of collaboration. The following diagram describes a product problem detected based on collected information.

  • A high volume of data from a variety of sources (see examples mentioned in Figure 2 Service & Maintenance Data collection), received with different velocity (real time vs. Asynchronous) is collected in a cloud server system. This fulfils the definition of big data technologies.
  • Data Analytics Systems supported by pattern recognition algorithms and artificial intelligence process the data and present visual data representations and hints for potential problems. The aggregation of data must be adjusted to the target audience.
  • The engineering department takes the main responsibility of the analysis in the case of a product problem. However, in the case of complex issues it requires cooperation from different areas and the extended enterprise in order to capture nuanced information that points to the root cause of the error. It also requires knowledge management to investigate past experiences.
  • Successfully finding the root cause leads to a solution, which might be a product update, a process improvement, a vehicle software update or new procedures and techniques for repair and maintenance information that must be distributed with little delay.
    • Software updates or vehicle configuration changes can be accelerated by deploying technology such as Firmwareover- the-Air update to correct errors in the vehicle electronics.
    • Digitalization can provide vehicle-specific updated repair and maintenance information directly to the service network and provide guidance appropriate to the experience of the user.

4. Challenges and Opportunities

The integration of so many data sources and actors in a coherent IT landscape is extremely challenging, because it deals with a high variety of data sources and business processes that have been implemented over several years. New technology such as connected vehicle, Internet of Things and autonomous driving increase the sheer amount of data stored per vehicle, compounding the problem. Existing systems are mission critical and are provided by different suppliers, making project coordination complex and error prone. The advent of commercial cloud systems and proven data analysis technologies reduce many of the barriers for data sharing across the extended enterprise. However, a strong access level rights system and data security is key to provide the correct amount of information to each party.

Additional standardization efforts will also simplify the integration of software suppliers providing solutions in the cloud and the creation of migration procedures for legacy data. As an example:

  • Vehicle On-Board Diagnostics information such as telemetry data, vehicle health and system state, vehicle software monitoring and alerts can be used to maintain a software model of the vehicle (also called as digital twin) on the cloud that can be used for analysis and virtual manipulation. Standardization will simplify the integration of telematics hardware providers, communication platforms and data analysis tools.
  • New business processes and the changing relationships in the business ecosystem (see Figure 1: Autonomous Trucks Business Ecosystem) require the creation of new, common data models for the exchange of data. This modularizes the system required to support the business process and provide a simpler way to integrate the data into the analysis.

The increased importance of collaboration and software also requires a new approach to supplier management and purchasing, as the OEM no longer purchases physical parts, but complex software systems that are deployed both to the vehicle and the cloud and that can potentially use new licensing models.

5. Conclusion

Autonomous Driving will change the nature of the transportation business in drastic ways. For the Commercial Vehicle OEM, it will lead towards a stronger focus towards delivering the smallest possible Total Cost of Ownership (TCO) to command a premium on the vehicle price. This pressure will also force the OEMs to push outwards into additional business areas and to increase the amount of collaboration with the extended enterprise.

Collaboration creates a competitive advantage because sharing experience and data can be used to create an improved product improvement cycle to increase the vehicle uptime, reducing the TCO for the final user. Leveraging the information and the experience of the actors requires mixing together different data sources and business processes into data analytics systems that are accessible to all participants based on user roles.

Taking advantage of new technologies can potentially simplify the creation of collaborative ecosystems but is still difficult to integrate disparate software suppliers while addressing challenges such as security and data migration.

The creation of new standards can greatly simplify future implementation and is a priority for the whole industry. Our continuous success depends on reaching beyond our organizations. As Benjamin Franklin said in 1776 before signing the Declaration of Independence:

We must, indeed, all hang together, or most assuredly we shall all hang separately.

 

Mario Alberto Ortegón-Cabrera, Head of System Strategy and Innovation Management, Mario.Ortegon@dsa.de
Frederic Schaus, Project Manager Connected Solutions, Frederic.Schaus@dsa.de