Table 3.

Framework application at GM

Capability	Level of maturity					Products, services & initiatives	Status quo evaluation of capabilities
	1	2	3	4	5
Primary IS capabilities for Industry 4.0
Connect & Store
Product connectivity & system inter-operability				x	x	Remote support services	Chances: GM’s customer’s (i.e. discrete manufacturing machines) can be equipped with solutions for remote management (connectivity); solutions provide interoperability with major MES/CAD/CAM systems Challenges: Regulations and customer security policy often limit access to products; it can prove challenging when various connectivity platforms need to be run in parallel
Seamless connection to customers			x	x		Webshop; Smart service apps	Chances: The webshop can be used to order consumables and spare parts, which is a high-profit business; the company can free up the workforce by automating such tasks as technical support services or the documentation of maintenance actions, all of which can be done with the smart service app Challenges: Getting customers to use digital channels involves change for small and medium businesses and their customer relation departments, which have spent decades performing the same activities via phone and fax
Continuous collection & storage of real-time data			x	x		Track & trace services	Chances: Scalable solutions for data storage are in place and can be adapted to the specific needs of the respective customer and industry; learning from customer behaviour on the broader industry (e.g. regarding frequency and access) can help a company find the right solution with greater ease and speed Challenges: The key is to generate value from data (e.g. digital services) and then monetise that value because the upfront costs for data storage can be immense (e.g. sensor data); however, there are still major hurdles that can get in the way of convincing customers to pay for complementary services
Understand & Act
Automated processing of (un-)structured data		x	x			N/A	Chances: The evermore established standards for interfaces (e.g. OPC UA) on the shopfloor can simplify the access to different data sources as well as their aggregation Challenges: It is still challenging to aggregate data from different machines, which is due in part to the varying ages and constructions of the machines and in part to the limited compatibility of multiple manufacturers and software providers
Descriptive & diagnostic analysis		x	x			Performance & condition monitoring apps	Chances: Performance indicators that make it possible to measure a product’s condition or quality are defined upon consideration of customer feedback Challenges: Even when one’s own dashboard is in place, it is more important to integrate it into the customer’s solutions, which is not always easy
Automated monitoring & reasoning		x	x			Material analytics app	Chances: First analytical solutions are in place to optimise production processes and achieve direct savings for the customers (e.g. mapping of materials to product orders; optimising the use of the material) Challenges: Past a certain degree of complexity, automation requires a lot of customer- and process-specific adaption
Predict & Self-optimise
Predictive & prescriptive analysis	x		x			Predictive services based acoustic data	Chances: Based on sensory data (e.g. acoustic data), a problem occurring in the cutting process can be detected, after that the likelihood of machine failure can be predicted – acoustic anomalies detection promises to have a significant potential Challenges: This capability is still an investment topic for GM, and the CTO is unsure whether customers will pay for it – in any case, it will require a lot of patience to see a return on investment
Self-optimising products & production systems	x		x			AI-specific services	Chances: AI-based services help reduce costs and minimise unpleasant tasks currently performed by staff in order to defend GM’s role as a market leader Challenges: The requirements for data quantity and quality still pose great challenges to the company; collaboration with research institutes and other partners is required to use the full potential of this capability
Autonomous provision of products & services	x		x			Smart factories	Chances: The company can use smart factories to demonstrate new product solutions; these can take over during production peaks or produce small batches for specific customer needs (e.g. prototypes) Challenges: It is not yet clear whether the high investments will pay off; it can also be challenging to justify such high investments to the supervisory board; the degree to which “dark factory” approaches (i.e. without any human interventions) are desirable is unclear
Support IS capabilities for Industry 4.0
Strategy
Evaluation of customer & technology trends			x	x		GM’s trend radar	Chances: The company can use its innovation radar based on customer surveys and market research to identify and prioritise trends; using personas for different customer segments helps to improve customer understanding Challenges: It may prove difficult to ensure that research is not driven too much from the inside (i.e. domain experience) as the lack of an external perspective may make it challenging to recognise disruptive trends
Data-based product & service innovation		x		x		Inter-disciplinary business development team	Chances: An interdisciplinary business model team could be appointed to bring together the necessary skills to develop data-based business models (e.g. adapt digital innovation methods or new business models) Challenges: Breaking the mould when developing new products challenges existing structures and people’s understanding of their roles
Readjustment of sales & service provisioning structures		x		x		N/A	Chances: End-to-end services can be introduced for new product and service solutions Challenges: It may be challenging to establish new service structures, especially when unique expertise has to be developed and the staff increased; further challenges may arise as old and newly developed systems need to be serviced simultaneously
Strategic leveraging of partner network	x x					Pilot projects for new business models	Chances: Pilot projects can collect first experiences and insights, for example, with an important customer and globally acting financial service provider, which can help to pioneer a new business model (i.e. pay-per-part model) Challenges: There may be some difficulties in finding the right service/implementation partners, but less so with the technology partners; it may also prove challenging to define a clear technology target and align all internal stakeholders accordingly
Technology
Seamless operations & process digitalisation			x x			Focus on ordering processes	Chances: Management has identified the great potential for cost savings with automation of support processes before and after manufacturing; faster and more efficient order and billing processes can also enhance customer experience Challenges: Speeding up such processes can be hampered by silo structures
Seamless human-machine collaboration			x	x		Tests with camera systems	Chances: Novel camera systems have entered the test phase and already proven capable of perceiving their surroundings in ways similar to the human eye in that they can recognise depth, motion, and surface structures, which makes them valuable to machine operators as it lets them see more clearly which tasks to prioritise Challenges: Finding experts for computer vision and related domains may be difficult and costly
Governance of data, product security & liability			x	x		Vulnerability management	Chances: As security can make the decisive difference for customers, it is a selling point when vulnerability management is in place to process any vulnerabilities and security gaps in one’s own or third-party components Challenges: Data usage agreements are a challenging topic since customers raise concerns about liability issues
Intra-organisational information exchange			x x			Internal fairs and exchanges	Chances: Conducting internal exchanges among digital project managers were very helpful in managing interfaces Challenges: Company communication platforms and channels do not yet support the desired exchange
Human Ressource
Established mutual trust, adaptability, & openness		x x				E-learning platforms	Chances: By installing e-learning platforms for off-line and online learning in multiple languages, content can be offered 24/7; content can then be made accessible for other countries and languages with reasonably low effort Challenges: First, one has to win customers’ trust in business models that provide new ways of delivering value
Leadership for change and agility		x	x			Trainings and academies	Chances: New skills can be developed in an academy format structured around digital technologies Challenges: It may prove challenging to train seasoned managers in agile methods; before new approaches are adopted, one has to invest time and practice to be adopted
Profound digital literacy & digital competences			x x			Trainings and academies	Chances: Optimally equipped seminar rooms; training on the machine directly Challenge: Bringing an organisation on a basic skill level is a ‘mammoth task’ and affords high investments
Fostering interdisciplinary teamwork			x	x		Inter-disciplinary institutes	Chances: Interdisciplinary institutes that bring together different faculties can stimulate people to think ‘outside the box’; the pathways of collaboration can be notably shortened (e.g. shared cafeteria, conference rooms) Challenge: Bringing experts from different domains together and getting them to produce innovative work can require more than shared rooms

x = Status quo (today); x = target state (in 3 years); N/A = no information available