The presentation focuses on how enterprises can turn Internet-of-Things-Data into Action and outlines the 5-A Model for Data Actionability. 5A stands for Action, Assignment, Analysis, Aggregation and Acquisition.
Central questions such as “How do I identify bad quality during or before the process?” or “How do I prevent unplanned downtime?” are addressed in this presentation by Prof. Michael Capone, at the Capgemini Week of Innovation Networks 2016.
2. CSX - Data Actionability
Performance and usage data is
acquired and transmitted every
x seconds.
Data from many
connected devices
is aggregated and
stored
When the data indicates an issue that
can cause a problem or opportunity, a
case is created and a resolution or „Next
Best Action“ is selected
An actor takes action:
1. Customer Care provides
the operator tips and
instructions
2. Sales & Marketing
proactively sends the
customer offers and
orders
3. Technical Service is
dispatched to perform
maintenance
4. System sends messages
to machine or person
We turn IoT data
into ACTION
1
2
4
5
Data is analyzed in
real-time to identify
issues or
opportunities.
3
Control Tower
Connected Service Excellence (CSX)
„2 Steps Ahead“
4. IoT-Cloud
State Engine
2. Machine Data
5. Alert & Task
Technician
ServiceCloud
Manager
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I identify bad quality during or before the process?
A: PQM
Aside from the
technician, who else
can benefit from
preditive quality?
What other
processes can
machine data trigger
for which actors?
5. IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. Article
ServiceCloud
Manager
Assets
6. Model
5. Location + Error Code
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I prevent unplanned downtime?
A: PMM
Aside from the
technician, who else
can benefit from
preditive
maintenace? What
other processes can
machine data trigger
for which actors?
6. IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. Article
ServiceCloud
Assets
6. Model
5. Location + Error Code
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I ensure service is performed correctly?
A: PMM w/ VR
Aside from the
technician, who else
can benefit from
preditive
maintenace? What
other processes can
machine data trigger
for which actors?