To achieve this goal clear standards as well as consistent and joint decision processes across different businesses are needed. At BASF, a major step toward standardizing corporate processes in supply chain, procurement and finance was the migration of several different ERP systems to one BASF system in 2013. It was an essential prerequisite for BASF’s digital future. On the basis of a consistent technological infrastructure, we were able to launch new processes for vertical‐ (in factory) and horizontal integration, as well as new tools supporting more effective collaboration in the company. The consolidation of data (structured and unstructured) provided the basis for assessment of large volumes of data throughout the organization and for big data analysis. But encountering horizontal integration only from a tool perspective is not enough to cater to the requested speed, agility and cost efficiency within all industries.
Digital technologies offer huge potential in accelerating the transformation of research outcomes into competitive innovations. In product development, big data analysis provides new opportunities to get to market faster and move closer to the customer. A BASF research division subsumes all topics coming from material sciences and systems technology. Its experts focus on developing new materials and improving material properties for research growth areas as diverse as lightweight design for the automotive industry, thermal management solutions for buildings, functional crop care, water treatment and coatings of all kinds.
To make sure customers get exactly the materials the market demands, extensive test series are a standard procedure, followed by real‐life application trials. Testing continues until a material with the ideal manufacturing properties has been found. This complex process generates huge volumes of information, which are stored in different systems. Part of these data are stored in an electronic lab journal, which at this time does not offer computer‐based analysis of the unstructured data or enable design of experiments.
What if all research workers shared their experiments, analyses and test results and everybody could access the accumulated data? As part of an innovation project, we have started to develop a prototype – called Virtual Experiments Platform – involving four research projects. The platform analyzes lab data using a digital data model and supports researchers during the actual work process. The newly developed modeling program can calculate multiple parameters simultaneously and model highly complex improvements. The platform cut the number of experiments in the second test series of one project by up to 90 % – and led straight to the desired outcome. Thus, the customer was presented with a valid result in a much shorter time.
We have shown that data modeling and optimization by computer can significantly accelerate research, which enables us to bring innovations to market much faster. What’s more, digital data management secures know‐how and provides access to the knowledge pool for every researcher who works with the system. The more people that actively use the platform, the more efficient the system actually becomes.
46.4 Success Factors for the Digital Transformation
46.4.1 Capturing the Potential of Big Data
Data is undeniably the currency of the 21st century, but to derive its true benefit, the right conclusions from the available information need to be drawn. Because of this, BASF has focused on data analytics and has been developing innovative, data‐driven solutions to support BASF’s businesses in decision‐making.
Mathematical methods to evaluate large amounts of data for substantially more accurate prediction of future developments are a key element in these efforts. The ability to manage large volumes of data is also the basis for future‐oriented methods like machine learning, examples of which include deep learning and cognitive computing that use models to approximate or mimic the functioning of the human brain. With cognitive computing, machines can learn, for example by cross‐linking and analyzing source input – and by recognizing patterns. Machine learning is an area of major interest to BASF. It changes the way we use mathematical methods and enables to develop smart, self‐learning solutions.
One example is a new solution called “Data Driven Sales,” which gives the sales force access to the complete data set about their customer in real‐time. With the aid of predictive algorithms, diverse information can be visualized such as the number of previously purchased product units or customer‐specific product recommendations. The tool enables sales workers to respond faster to customer queries – anytime, anywhere.
46.4.2 Smart Manufacturing
BASF leverages potential applications for big data based tools in operations. “Smart manufacturing” is the key term here. It covers the production environment and engineering maintenance. An example would be the use of digital data analysis for predictive maintenance of factories and systems. “Smart manufacturing” shows how data analysis can be used for predictive maintenance in production operations in order to reduce or – ideally – completely eliminate unplanned downtime. For example, the steam cracker, which is one of BASF’s key factories, is equipped with leading‐edge measurement and control technology. The steam cracker has thousands of sensors monitoring process data, such as pressures and temperatures, so that the factory can be run from the control room and the condition of it can be monitored. Our experts have developed mathematical models with colleagues working in the factories to identify performance losses and abnormalities that would have gone unnoticed before. In the future, a dashboard will show if a particular column or factory component malfunction might occur. That information helps to manage the factory in a more targeted way and initiate counteractive measures. Gradual problems that develop over time can also be identified earlier. Moreover, the algorithms help to indicate the optimal time to choose for maintenance.
46.5 Challenges of Digital Evolution
The examples described in this article are just a taste of the opportunities tapped into so far as we transform BASF into a digital organization. The future holds many more, even though there are also challenges on the horizon where smart solutions are needed. The digitization and connection of everything makes businesses increasingly vulnerable to cyberattacks, a major challenge, which is increasing in both incidence and sophistication. To keep pace with these developments, businesses are raising their investment in IT security and joining forces to combat cybercrime. Only then can the potential of digitization be leveraged while companies also protect themselves effectively against cyberattacks.
The 2014 hack of Sony Pictures Entertainment and the 2015 infiltration of the German parliament’s computer system shows that cyberattacks can strike anytime, anywhere. Cybercrime has grave implications and costs the global economy about $450 billion a year, experts say. Intellectual property theft can delay technical progress and skew competitiveness. Moreover, falling victim to sabotage and hacktivism can significantly damage an organization’s reputation. Security strategies and IT architecture need to meet ever‐increasing demands to protect IT systems. Consequently, investment in cybersecurity is rising. A study by the National Initiative for Information and Internet Safety says more than one‐third of surveyed German businesses expect spending on IT security and data protection to double by 2020. It is no surprise that Cybersecurity Ventures, a market research firm, expects the cybersecurity market to grow from $75 billion in 2015 to $170 billion in 2020.
Cybersecurity is also a top priority for all industry players. We have invested in extensive cybersecurity measures as part of a continuous drive to improve security. A comprehensive program has three main targets to improve resilience against cyber‐attacks: raise security awareness among employees and implement robust security processes and organizational set‐up, protect highly critical systems and information, such as research data, and improve overall security for systems throughout the organization.
BASF est
ablished a cyber defense organization to prevent cyberattacks more effectively and to minimize the impacts of potential attacks on business. The cross‐functional organization is composed of IT experts with a variety of specialist areas including cyber security operations, security strategy and architecture, security analytics as well as risk management, who work hand in hand to recognize and respond to attacks on BASF’s IT architecture as quickly as possible. Rapid response is key in the struggle against cybercrime. The longer an attack goes undiscovered, the more difficult it is to establish which systems are impacted and the more time the attacker has to harm the organization.
Modern security techniques analyze and monitor the IT network to counter known threats and predict new ones. BASF’s team uses a threat intelligence platform, consisting of a monitoring and early warning system. The platform consolidates data from different sources and analyzes irregularities to enable early recognition and assessment of attack patterns as well as a timely and appropriate response. The intelligence obtained from data analysis helps to identify and pre‐empt future cyberattacks before they have a chance to wreak havoc.
46.5.1 Strategic Alliances
Apart from close internal collaboration and bundling cyberattack defense resources, BASF, as announced in the press, leverages strategic alliances with other companies and organizations. In 2014, seven DAX 30 companies – including BASF – set up the Cyber Security Sharing and Analytics Association (CSSA) for this purpose. The objective of the association is to share information and intelligence across industries, build up more expertise, as well as improve defense techniques and counterstrategies for a faster and more effective collective response to cybersecurity challenges.
BASF also teamed up with DAX companies Allianz, Bayer and Volkswagen in 2015 to set up a dedicated cybersecurity service provider: the German Cybersecurity Organization (DCSO). The hope is that close collaboration between industry and government experts will contribute to meaningful improvement of corporate security architectures. The organization bundles expertise, offers security audits, as well as provides new services and corporate security technologies that are not commercially available today in that form. DCSO collaborates closely with the appropriate agencies in Germany. Apart from enabling the participating companies desire to better protect themselves, the collaboration via DCSO allows them to share access to IT infrastructures.
BASF collaborates with other industry leaders on the research end as well. An organization called the Digital Society Institute was set up with the support of BASF, Allianz and consultancy Ernst & Young (EY) at the European School of Management and Technology in Berlin in 2016 to deliver information as well as develop analyses and strategies for the digital future. This scientifically autonomous research institute investigates issues to do with digitization, including cybersecurity, and promotes dialogue with stakeholders in business, industry, politics and society at large.
46.5.2 Conclusion – Opportunities of Industry 4.0 for the Chemical Industry
While addressing cybersecurity concerns and preparing for continuously increasing and inevitable risks, the Industry 4.0 transformation offers huge opportunities – for all industry segments. For one, we are a process industry and already have relatively high levels of automation. Digitization promises a major efficiency boost for production operations and maintenance as well as opens up new opportunities for the automated manufacture of flexible, highly differentiated product offerings. Moreover, research and development will benefit too. For example in areas including new material modelling simulation and analyzing research data as it has been done for many years in the high tech industry.
Secondly, the chemical industry is part of a highly interconnected value chain. The interlinking and analysis of unimaginably large volumes of data, created not just within a company itself but within every step from raw material sourcing to end product usage in a customer’s product, raises transparency and drives monetary value to be realized. The need for a holistic horizontal integration of all involved partners along the value chain is simply the next consequential step in the Industry 4.0 journey for supply networks to become ever more competitive. The newly emergent data, processes and insights optimally coming from joint customer centric interfaces, which moreover should rely on joint decision making processes, will also give rise to new business models. That makes us transform into a know‐how hub in the industry.
Thirdly, digitization forges deeper relationships between industry partners and our customers in many ways and offers huge opportunities for higher customer value and higher reliability towards ever changing customer needs. Digitization has already transformed many areas, and this is only the beginning.
The entire marketplace stands to benefit from perhaps the most important of all perspectives: digitalization will allow us all to benefit from more effective and sustainable approaches to consuming planet earth’s precious resources!
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_47
47. The Challenge of Governing Digital Platform Ecosystems
Maximilian Schreieck1 , Andreas Hein1 , Manuel Wiesche1 and Helmut Krcmar1
(1)Technical University of Munich, Munich, Germany
Maximilian Schreieck (Corresponding author)
Email: [email protected]
Andreas Hein
Email: [email protected]
Manuel Wiesche
Email: [email protected]
Helmut Krcmar
Email: [email protected]
47.1 Multi‐sided Platforms1 2
Digital marketplaces such as multi‐sided platforms (MSPs) are continuing to grow in importance [1]. Prominent representatives are start‐ups like Airbnb or Uber who are challenging traditional business models in the taxi or gastronomy industry. These digital companies extend the classical point of sale by providing a platform where everyone can offer services or products to the corresponding market. Also, traditional industries like the equipment manufacturer Trumpf engage and invest in MSPs [2]. On the contrary, there are also companies who got market power but failed to establish a digital business model. Garmin, for instance, dominated the navigation market and was overran by Apple and Google offering various navigation applications [3]. The economic importance of MSPs can be highlighted by Alibaba initial public offering (IPO), which holds the title of the largest IPO in history [4].
The foundation of each MSP is the underlying platform which orchestrates the interactions between the different sides [3]. Within this platform, the interplay of actions is controlled and managed by various platform governance mechanisms [5, 6]. In order to understand why platforms are disrupting long‐established industries, it is crucial to look closer on how those mechanisms work.
Even though platform governance mechanisms are theoretically well researched [5, 6], the practical implementation lacks examination. The degree of openness, for example, can be on the hand too low resulting in an insufficient growth or on the other hand too high, losing control over the platform [7, 8]. This article aims to improve the theoretical understanding by showing tradeoffs resulting from a different implementation of platform governance. Also, practitioners gain valuable insights on how to set up their platform governance strategy and which tradeoffs they need to take into consideration.
47.2 Multi‐sided Platform Governance Mechanisms
In order to get a better understanding of a platforms governance, a literature research was conducted to identify important mechanisms according to science [7]. The results are displayed in Table 47.1 and range from dimensions like Governance Structure to External Relationships. Table 47.1Platform governance mechanisms. (Own representation based on literature review)
<
br /> Dimensions
Mechanisms
Description
Governance structure
Governance structure
Decision rights
Ownership status
Is the set‐up centralized or diffused? How are authority and responsibility divided between the platform owner and module developers? Is the platform proprietary to a single firm or is it shared by multiple owners?
Resources & documentation
Platform transparency
Platform boundary resources
Does the documentation ensure an easy understanding and usability of the platform? Are governance decisions concerning the platform’s marketplace easy to follow and understandable? Are Application programming interfaces (APIs) used to cultivate the platform ecosystems through third‐party development?
Accessibility & control
Output control & monitoring
How are outputs evaluated, penalized, or rewarded?
Input control
Securing
What mechanisms are in place to control which products or services are allowed? How to assess the quality of services or products?
Platform accessibility
Process control
Platform openness
Who has access to the platform and are there any restrictions on participation? Who controls the process and is in charge for setting up regulations? Is the platform open or closed?
Digital Marketplaces Unleashed Page 72
