Factor scoring & acitivity implication

Earlier this year, Audi presented a newly established partnership with Ericsson to implement 5G within the safety of human-robot interaction (Niermann, 2020). This is amongst processes applied in manufacturing and a PaaS that could become part of DPP. Besides the pilot-project explicitly run by Audi, Volkswagen Group also applied for a license to operate local 5G network in April 2020, like the one of Bosch (Rauwald & Nicola, 2019). Although the development creates a positive outlook for Audi, the challenge, however, still remains in scaling the implementation of the entire DPP.

Essentially, the yield of economies of scale and mobility are the dominant and pivotal aspects of the production within PAM. For Audi, this is achievable through modularity within manufacturing plants of those direct cost-advantages being a part of the Volkswagen Group. As manufacturing of vehicles constantly changes, shifting towards a greater usage of software technology and increasing the complexity of production, these important aspects can continuously be achieved through the DPP.

The digital element of the development towards Industry 4.0, therefore, enables the process of effective gathering, grouping and interpreting real-life data from the network of the DPP, thus empowering informed and rapid production optimisation. DPP is not yet fully implemented within the group, and a key challenge will be to deploy DPP on full scale with all factories and external suppliers. This heavily relies on the adaption of 5G, which is a critical aspect, delivering the necessary reliability in data processing to qualify the value generated from the platform.

Value of the platform comes from the utilisation of SaaS, which effectively would enhance economies of scale obtained through direct network effects. This is, however, only obtainable due to the organisation being accustomed with a certain level of adaptability through Audi’s modular system.

Furthermore, the combination of the DPP and MQB are arguably essential aspects covering the advantages related to Audi being a part of the largest group within PAM. Additionally, with the characteristics of a multi-sided platform, DPP creates value through the PaaS-aspect formed by mutual interaction and affiliation of both users and complementors.

8.3.2 Factor scoring & acitivity implication

8.3.2.1.1 Technological adaption (5G)

Within the development of Industry 4.0, industrial production platforms rely on strong connectivity capabilities in the form of 5G adaption. Additionally, PAM players will need to adapt to this new technology, which will require internal update of production machinery and the related software. This is significant in order to cope with a massive increase in data capacity created from connected devices, transferring huge samples of data constantly from one to another. Reliability is then a key aspect, which 5G can accommodate with its resilient stability and low latency abilities.

Although this adaption is vital for companies pursuing the usage of platforms to optimise production, it is arguably a factor that depends on externalities and regulations. This kept in mind, for a platform to deem success within production, technological adaption is a necessity and thus scoring it 5.

8.3.2.1.2 Indirect network effects

A relevant platform, used within production, drives value effectively in the presence of obtainable indirect network effects from increasing numbers of software suppliers for production processes.

With the emerging possibilities of remote cloud computing and PaaS, a platform can enhance the collaboration and interaction between the PAM factories and suppliers of machinery, robots, software among others. This will allow for faster adoption of optimised production processes for the factories delivered by suppliers when both share affiliations to the platform. There are still risks and unfulfilled potential related to the the complementor side of a production platform within PAM, therefore the factor-score of 3.

8.3.2.1.3 Market concentration - direct network effects

Production platforms within PAM are responsible for connecting brand-related factories, constructing what is known as smart factories. The greater the same-sided network from production facilities are, the more value can be derived through the use of platforms.

Due to the characteristics of cloud computing, same-side direct effects occur with the value for each factory (user) goes up as more factories join the platform. A network is then able to take shape and establish the foundation of increasingly more data, shared to optimise production processes.

However, the direct network effects are limited to merely having a positive effect as long as the factories are not competing, forming collaborative and not opportunistic behavior. Due to the relative high concentration of the PAM, this factor is arguable 4.

8.3.2.1.4 Manufacturing modularity

Based on the purpose of a production platform, it is evident that the manufacturing to a larger degree has to be organised as a loosely coupled modular system. This is apparent due to cloud computing possibilities, seeking to augment every individual aspect of the production lines to optimise the production efficiency. If each manufacturing robot, for instance, is unable to be programmed differently or altered in their process through the platform, the value will diminish.

Based on the structural planning process of PAM, manufacturing modularity can be argued as highly present within the upstream planning and thus the score of 5.

8.3.2.1.5 Technological complexity of production processes

As a derived effect of more technologically driven cars, the production of vehicles within PAM has become increasingly complex. Besides AVs and EVs, the general production processes are gradually becoming more driven on technological systems. If a platform within production to a larger extent is able to cope with the increased complexity related to AI and machine learning of software, robots and other processes, it can be deemed successful.

It is evident that production within PAM is under major alterations with rising collective software architecture and implications are therefore present for platforms to enhance optimisation. With this is mind, the factor-scoring can be argued to be 5.

8.3.2.2 Activity implication

Effectively, production activity within PAM seeks to generate efficiently managed processes of production, leading to desired quality and quantity under optimisation of costs. It is a dominating aspect of general production within PAM to utilise scale economies and mobility to meet geographically and constantly changing demands. Sustainability within production is another vital aspect, caused by the external pressure and forcing PAM players to optimise their environmental management of production. A well-managed production activity will effectively support the conversion of emission reducing processes.

Under the conditions of Industry 4.0, utilising platforms enable PAM to significantly optimise the production processes, and thereby obtain economies of scale through direct and indirect network effects among connected factories and suppliers of machinery. The potential to benefit from cloud computing through a platform and eventually developed smart-factories, will lead to new possibilities of optimisation and support mobility of production. Besides the optimal processes, which can also

production resources, such as machinery, and materials are not covered by the platform, but are ultimately aspects that will affect the success of the production activity within each PAM player.

Based on current platform indications within PAM and the potentials of Industry 4.0, the relativeness score of platform utilisation is argued to be 3.