Business impact for data-driven services in Manufacturing Jon Kepa Gerrikagoitia (Research Manager, IDEKO)

1. Jon Kepa Gerrikagoitia, Ph.D.
Head of ICT and Automation
Helsinki, October 16th 2019
Business impact
for data-driven
services in
Manufacturing

2. WHO WE ARE
 Danobat Group, a leading Machine Tool Group
with two brands: Danobat and Soraluce.
 15 high-level production plants
 Workforce 1300 people approx. € 260 million
turnover
 Sectors: Automotive, Wind, Aeronautics,
Health ...
 Customers:
 Automotive: Caterpillar, Ford, PSA, Volvo
 Energy: GE, Gamesa, Vestas
 Railway: CAF, Siemens, Alston, SNCF
 Aeronautical: GE, Rolls Royce, Lufthansa

3. CHANGES IN MACHINE TOOL BUSINESS
MODEL
General purpose equipment
Future premium
prices at stake
Commodizted
Specific purpose equipment
Hardware
commodization
thread
Machine
Software
Services
Asia -
Pacific
America
Europa
ROW
Machine Tool Production (%) MT revenues per value source (%)
Displacement of source of value to software and services
Source: Indra Minsait
Increased competition specially
from China….
….eroding margins in machine
tool sector….
….leading to the rethink the
current business models.

4. DIGITALIZATION
Digital products and services address the needs of customers in markets that promise
significantly stronger growth than mechanical engineering companies’ typical core
businesses of machines and components
Growth opportunity
30%
sales growth of more than 30%
for offerings from mechanical
engineering companies that
relate to IIoT
2-3%
sales growth of core
mechanical
engineering products

5. DIGITAL BUSINESS CONCEPT
5 Development lines can be clearly defined under the concept of Digital Business

6. NEW BUSINESS MODELS
Data for leveraging new approaches
 Outcome-based model the customer is charged by usage, or outcome
 Extended product core product combined with value adding data-driven services
available throughout its lifecycle
 De-manufacturing breakdown of a product into its individual parts with the goal
of maximizing reuse and recycling opportunities
 Mass customization combination of flexibility and personalization that are
peculiar to bespoke manufacturing with the low unit costs associated with mass
production
 Virtual factory orchestration of a community of micro-factories, all linked by an
electronic network that enables them to operate proximity production when
receiving an order
 Crowdsourced innovation exploitation of collective intelligence with the aim of
gathering valuable design ideas and technical insights to be turned into real
products
 Analytics-as-a-service on-demand access to a one-stop-shopping analytics engine
supporting shop floor intelligence
 Open data platform data hubs meant to integrate disparate data sources, thus
enabling information to become shareable and actionable
 Dynamic pricing SLAs, pricing plans, payment terms, and other contractual terms
may be customer-specific or based on real-time market factors

7. REVENUE STREAMS
Everything as a service
• Pressure towards flexible solutions and cost improvement
• First experiences in mechanical engineering (Heller, Schaefler,..)
Source : Siemens

8. CIRCULAR ECONOMY
Favorised by the new Business Models
Asset provider is responsible for all lifecycle costs, supported by IoT
 Design products for CE: based on collected usage data they can be redesigned to be maintained,
upgraded, disassembled, and recycled in an easier way
 Minimize operating costs (for instance, by increasing resource efficiency): extending the product
lifespan, and collecting back products to allow multiple lifecycles
 Monitor the product condition, status, location, and usage for proper use avoiding wear and tear
 Introduce sharing BMs: depending on use and need of production of different manufacturers.
Increases the product utilization, improvement in resource efficiency
 Improve the provision of technical support and other services, such as repair, assistance, spare parts
management, etc. the lifespan of the product may be extended. Furthermore, companies may provide
to their customers personalized advice with the aim of optimizing the usage phase
 Reduction of the product consumables (e.g., energy) during the usage phase, thus increasing resource
efficiency.
 End-of-life collection, refurbishment, remanufacturing and recycling activities in a proper way,
collection activities when products reach end-of-life may be optimized, since companies know each
product location in real time.
IoT

9. SERVITIZATION
• Connected machine
• Monitoring
• Smart Functions
• Spare parts
• Remote diagnostics
• Consulting for operational
improvement
• Maintenance program
• Condition dependent
maintenance
• Guaranteed availability
• Guaranteed performance
• Power by the hour (pay per
output)
• Operational costs included
A process in Several Steps
Product Add-ons
Basic services
Product Add-ons
Basic services
Product Add-ons
Basic services
Product Add-ons
Maximized performance Maximized performance
Advanced ServicesPRODUCT
SERVICE
Revenue
services
Digitalization
Based in
property
Based in
use
Based in
results
Source: Indra Consulting based in Kohtamäki et Al

10. CHANGE IN THE OPERATING MODEL
From product-centered to client-centered
 Change from a sequential operation model to a data-driven interconnected model with short
planning cycles
 Disruptive for the traditional manufacturing sector, companies are still in the very early stages of
business model transformation
 Need to work on pilot cases
 Fully align their operating models with these new business models
 Impacts the whole organization
of executives say they expect to manage multiple
operating models in parallel in the future.
(Accenture)
81% 4% of respondents reported
full servitization (IFS)

11. Paving the way
• Identifying and maximizing the opportunity
• Structuring it within Danobatgroup,
• Defining the business model that monetizes it
• Creation of a recurring revenue center
• Defining the value proposition and selling argument
• Identifying the target clients and getting the model to perceive it as
part of the value
• Define the appropriate channel (s) to address them
• Maximizing the monetization of knowledge
Define the Services Business Model, as a strategic area of ​​the company:

12. Set up: Implementation Keys
Business Model Technology 4.0
Structure
(Back Office)
Processes
(Continuous
Improvement)
• Define the actors /
clients typology
• Define sources of
income
• Define value
proposition
• Define commercial
strategy
• Connectivity of
machines
• Corrective issue
management
• Big data: analysis
and decision making
aligned with the
Business Model
• Expert knowledge,
• Diagnosis and
decision making on
received
information
• Sort and optimize
the Corrective:
speed up problem
resolution
• Feedback to new
developments
• Learning and
optimization
processes
Methodology
Resources/
Organization
Tools
Methods and processes that
need to be defined,
communicated and put into
operation.
Changes in the organization
related to the SAT and the
resources necessary to carry out
the actions.
Changes, usage, activation
of the tools currently owned
by Danobatgroup (SUTAN,
ERP, ....)

13. Customer profiling - Service Box
Danobatgroup defines packages of maintenance, consumables and spare
parts offering concrete solutions to demaning customers with specific
requirements
Autonomous customer Corrective customer Core customer Preventive customer Full service customer

14. Service Box examples
Preventive customer:
• The customer is machine operation focused and is
able to react to unforeseen issues but with no
structure to coordinate and execute scheduled
tasks, spare parts or consumables.
• The customer wants to guarantee that the
machines have a perfect set up and commissioning
at any moment without internal resources for that
purpose
Full service customer:
• The customer is exclusively focused in the activities that
provide value to the product or service.
• The customer entrusts the auxiliary tasks to partner
suppliers integrating them within their structure in
order to ensure the effective functioning of the assets.
• The customer relies fully on its partner provider who
expects proactivity in order to optimize the results
offered by the assets.

15. Jon Kepa Gerrikagoitia, Ph.D.
Head of ICT and Automation
Helsinki, October 16th 2019
Business impact
for data-driven
services in
Manufacturing