Presentation #ECMLG18 in Utrecht on October18th 2018. Title: implementing big data analytics in a manufacturing environment: a theoretical framework

1. Implementing big data
analytics
in a manufacturing
environment: a
theoretical framework
Jürgen Moors
Co-promotor: Sofie Rogiest
Promotor: Steven Poelmans

2. Thesis
The successful implementation of big data projects
in a manufacturing environment
may be caused by different combinations of elements
that constitute complexity leadership and climate
in varying ways.

3. Importance of Manufacturing Industry
• European Manufacturing industry 2012 (Manufacturing statistics, NACE rev. 2, 2016)
 Employment of 30 million people
 7080 billion € turnover
 Impacted by industry 4.0
• Big data Analytics:
„Big data is a term that describes large volume of high velocity complex and
variable data that require advanced techniques and technologies to enable the
capture, storage, distribution management and analysis of the information“
(Federal big data commission, 2012)
• McAfee and Brynjolfsson (2012) indentified 5 key challenges for successful
adoption of big data analytics
Leader-
ship
Culture
Decision
making
Techno-
logy
Talent
Manage-
ment
Big data
Analytics

4. Focus and theory of research
• Davenport (2010 and 2014) focused on Technology and talent management
• Our interest is focused on organizational aspects
• Decision making can be seen as part of a company‘s culture

5. Leadership
Leadership
 complexity leadership model using the concepts of complex adaptive systems
(Uhl-Bien et al., 2007)
 Need for an adaptive space in order to link operational systems to
entrepreneurial systems in order for new solutions and innovations to survive
and thrive complex systems (Uhl-Bien, 2016 and Uhl-Bien and Arena 2017)

6. Climate
Climate:
 Schein (2010) defined culture as „a pattern of shared basic assumptions learned by
a group…“
 Schein (2010) looks at climate as a manifestation to the observer and is therefore
more behaviorally oriented
 Climate measurement on a individual and group level (psychological vs group
level“ (Patterson et.al 2005)
Relevance in Big Data environment:
• Data driven climate (Kiron et al., 2011; Davenport, 2014; Janssen et al., 2017)
• Cross-functional climate (Galbraight, 2014; Gabel & Tokarski, 2014, Kane et al., 2015; Fosso & Wamba 2015;
Calvard, 2016)
• Innovative and entrepreneurial climate (Donovan, 2015; White, 2016; Colegrove et al., 2016;
Brynjolfsson & McAfee, 2012; Kane, 2015)
• Autonomous climate (Davenport, 2014; Galbraight, 2014; Taylor, 2012)

7. Research in practice
• The different independent variables are interdependent (Fiss, 2007)
• Set-theoretic approach fits well to more systemic and holistic view of
organizations (Fiss, 2007)
• Concepts no simple on/off concepts  fuzzy-set variables (Ragin, 2000)
Complexity
Leadership
• Entrepreneurial
• Enabling
• Operational
Climate
• Data-driven
• Cross-functional
• Innovative &
entrepreneurial
• Autonomous
Big data analytics
success

8. Research design: Survey
Concept : Complexity Leadership
• No existing survey in literature present
• Survey design based on leadership literature for individual dimensions:
Operational: transformational and change leadership (Herold et al, 2008)
 Enabling: adaptive leadership (Sherron, 2000)
 Entrepreneurial: entrepreneurial leadership (Renko et al, 2015)
Concept: Climate
• Linking big data literature to climate literature (Patterson et al 2005)
 data driven : involvement and quality
 cross-functional: integration
innovative / entrepreneurial: innovation and flexibility
 autonomous: autonomy
Concept : Big data project success
• Based on the literature of project management and IT (Joslin and Müller, 2016)

9. Research design: Survey: next steps
Validations steps survey:
• In discussion with Mary Uhl-Bien for expert feedback on complexity
leadership
• Translation and back translation completed with several experts
• Survey test with test panel to guarantee wording is understood by all levels in
a manufacturing environment
• Survey dry run with one project in one company
• Roll-out of survey to gather data
• Analysis of the data

10. Questions ?

11. OPENING MINDS
TO IMPACT
THE WORLD