Lecture 2 - Industry 4.0

Question 1

Industry 4.0 key technologies

Answer 1

• Cyber-physical systems
• Cloud computing
• IoT
• 3D printing (“additive manufacturing”)
• Big Data, Analytics

Question 2

Electronic Data Interchange

Answer 2

was developed in the ‘80s to enable
electronic communication between companies in the supply chain, based
on standards such as EDIFACT

• Example EDI messages are PurchaseOrder, OrderAcknowledgment etc.
• In the first phase, stand-alone systems, later integrated with ERP
systems and inventory management
• No integration with manufacturing
• No real-time information
• EDI messages stored in internal systems, not easily accessible.

Question 3

Mirror world basic concept (Dai & Vasarhelyi, 2017)

Answer 3

• Every object in Physical World gets a unique identifier (oid)
• For each oid, there is an information object in MW (mirror world)
• The information (MW) object, or digital twin, maintains information about the state, location and history of the PW object
• Changes in the PW object are directly reflected in the MW object (sensing) and vice versa (effectuating)
• Agents (companies, ..) in the PW have a MW identity and collaborate in the MW on the basis of digital rights and smart contracts
• Design and testing (by simulation) of the object (type) in MW before objects in PW are built. What-if scenario simulation in MW possible for existing PW objects.
Not all of these properties are always present

Question 4

Distributed control

Answer 4

• Control in Industry 4.0 highly distributed
• With blurring boundaries (horizontal integration)
• Components as autonomous as possible. Why?
• Necessity – network load would become too high
• Opportunity – increase flexibility
• Distributed control means standards, interfaces and choreography
languages
• URI IoT identification
• Integration standards (e.g., ISA 95 for connecting smart machines to ERP)
• Global product standards
• Smart contracts for choreography

Question 5

Vertical integration and analytics

Answer 5

• Local data aggregation (decentralized intelligence), then global collection
• Multi-perspective (e.g., machine owner, product manager, process owner)
• Use of analytics to process big data, simulate, predict, prescribe
• Real-time control
• Life-time resource/process management (including design phase: digital
engineering)

Question 6

Value drivers Industry 4.0

Answer 6

• Time to market
• Service/aftersales, such as maintenance costs
• Resources (product life cycle management)
• Asset utilization (machine downtime)
• Labor (productivity in technical professions)
• Quality,
• Personalization
• Supply/demand match (forecasting accuracy)