Industry 4.0 application and reshoring of manufacturing evidence, - - PowerPoint PPT Presentation

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Industry 4.0 application and reshoring of manufacturing evidence, - - PowerPoint PPT Presentation

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Industry 4.0 application and reshoring of manufacturing evidence, limitations & policy implications Prof. Dr. Steffen Kinkel Karlsruhe University of Applied Sciences Institute for Learning and Innovation in Networks (ILIN) MAKERS

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  • Prof. Dr. Steffen Kinkel

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Industry 4.0 application and reshoring of manufacturing – evidence, limitations & policy implications

  • Prof. Dr. Steffen Kinkel

Karlsruhe University of Applied Sciences Institute for Learning and Innovation in Networks (ILIN) MAKERS Workshop “Industry 4.0 – Implications for an EU industrial policy”, Brussels, January 25th 2018

MAKERS - Smart Manufacturing for EU growth and prosperity is a project funded by the Horizon 2020- MSCA- RISE - Grant agreement number 691192.

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  • Prof. Dr. Steffen Kinkel

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Content

 Industry 4.0 and local value chains  Trends in manufacturing backshoring  Industry 4.0 enabling technologies and correlation with backshoring  Key competences for Industry 4.0  Conclusions for Industrial and Innovation Policy

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Rise of local value chains and Industry 4.0?

 Transnationally highly fragmented value chains are typical for today's global economy, in particular for high-tech products (Brennan et al., 2015).  E.g. iPhone: Designed and commercialised in the US  Assembled in China, “Made in the world”  But also disadvantages and risks of global supply chains show up:

(e.g. Handfield, 1994; Holweg at al., 2011; Nassimbeni, 2006)

 Long lead-times, low flexibility, instability in supply chains  Unsatisfactory quality standards of foreign suppliers  Cultural differences and communication problems  Rising labour costs  Increasing awareness for the back-/reshoring of manufacturing  Industry 4.0 / Smart Factory enables efficient and agile production systems  Potentials to support back-/reshoring?  Potentials to restore manufacturing and local value chains in EU countries?

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  • Prof. Dr. Steffen Kinkel

4  Offshoring stays on lowest level since mid 90s  Backshoring stable (slightly upwards); for every 3rd

  • ffshoring company

there is one backshoring  Around 500 German manufacturing companies per year perform backshoring

German evidence: Manufacturing

  • ffshoring and backshoring over time

17% 26% 27% 19% 25% 19% 12% 11% 11% 15% 9% 8% 9% 4% 6% 6% 4% 3% 3% 2% 3% 2% 3% 2% 3% 1995 (n = 1.305) 1997 (n = 1.329) 1999 (n = 1.442) 2001 (n = 1.258) 2003 (n = 1.134) 2006 (n = 1.011, n = 1.663) 2009 (n = 817, n = 1.484) 2012 (n = 820, (n = 1.594) 2015 (n = 571, (n = 1.282) Share of companies (in %) Jahr

German Manufacturing Survey 1995-2015, Fraunhofer ISI Metal and electrical industry Whole manufacturing industry

 Offshoring in the two years before ...  Reshoring in the two years before ...

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  • Prof. Dr. Steffen Kinkel

5  Issues: Different points in time, different economic conditions, different phases in the „offshoring and backshoring lifecycle”

“Adjusted” shares of backshoring companies in European countries

Country Share of companies active in reshoring Time-frame (years covered) “Adjusted” share of companies active in reshoring over a 2 years period Sweden 27,0% 6 9,0% Ireland 13,0% 3 8,7% Belgium 9,5% 3 6,3% Slovakia 9,0% 3 6,0% France 14,0% 5 5,6% Denmark 13,0% 6 4,3% Finland 13,0% 6 4,3% DACH 4,0% 2 4,0% Portugal 6,0% 3 4,0% Netherlands 6,0% 3 4,0% Selected European countries (EMS survey) 4,0% 2 4,0% UK 13,0% 8 3,3% Germany 3,0% 2 3,0% Estonia 3,5% 3 2,3% Lithuania 2,0% 3 1,3% Bulgaria 2,0% 3 1,3% Romania 1,0% 3 0,7%

Source: Kinkel et al. (2017): Measuring reshoring trends in the EU and the US, Deliverable 4.1 of the MAKERS project, Karlsruhe

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56% 59% 43% 52% 53% 68% 33% 28% 31% 25% 32% 27% 21% 20% 15% 13% 11% 6% 33% 6% 11% 5% 5% 0% 2% 0% 13% 19% 0% 10% 20% 30% 40% 50% 60% 70% Flexibility, Ability to deliver (2015) (2012) (2009) Quality (2015) (2012) (2009) Capacity utilisation (2015) (2012) (2009) Transport costs (2015) (2012) (2009) Coordination (2015) (2012) (2009) Infrastructure (2015) (2012) (2009) Labour costs (2015) (2012) (2009) Loss of know-how (2015) (2012) (2009) Proximity to R&D at home (2015) (2012) (2009) Availability of skilled workers (2015) (2012) (2009)

Source: German Manufacturing Survey 2015, Fraunhofer ISI

Main motives for backshoring of manufacturing activities (German evidence)

 Flexibility and coordination have become more important  Quality abroad still an issue  Labour costs and availability of skilled personnel abroad have lost in importance  Additional important reasons from

  • ther research: “Made in” reputation

effect, total costs of sourcing

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Industry 4.0 enabling technologies application levels – long ways to go

37% 15% 5% 23% 26% 19% 6% 20% 21% 37% 34% 30% 16% 29% 55% 27% 0% 10% 20% 30% 40% 50% 60% 1-49 employees 50-249 employees 250+ employees All companies Level 3 Level 2 Level 1 Level 0

  • Based on three

technology fields: (1) Digital management systems, (2) Wireless human-machine comm., (3) CPS-based operations

  • Levels = number of fields

in which companies have implementations

  • Large firms much more

active than small firms

Source: Kinkel, S. and Jäger, A. (2017): Auslandsverlagerungen, Rückverlagerungen und Digitalisierungsverhalten in der deutschen Industrie. Trends und Auswirkungen für den Produktionsstandort Deutschland, Karlsruhe

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Correlations of Industry 4.0 and backshoring

 Significant positive correlation between the use

  • f Industry 4.0 enabling technologies and the

backshoring propensity of German

(also Austrian and Swiss) manufacturing companies

 “Advanced users” (level 3) of Industry 4.0 enabling technologies display on average a 10-times higher backshoring propensity than "non-users" (level 0) Two arguments:  Use of I4.0 enabling technologies facilitate increased automation and productivity of the German factory site, making labour arbitrage of low-cost countries (LCC) less appealing and economies of scale more important  Even more important: Use of I4.0 enabling technologies facilitate increased flexibility and efficient production

  • f individualized solutions, providing incentives for

firms to keep/reshore production close to their European customers ( local value chains).

Cox & Snell: 0,055 Nagelkerkes: 0,230 Regression coefficient B Sig. Step 1 Ln #employees ,072 ,673 sec99_other manufacturing

  • ,038

,974 sec24_metal & metal components

  • ,093

,938 sec26_Data processing equipment, electronic and optical products ,691 ,561 sec27_electrical equipment ,439 ,724 sec28_machinery & equipment

  • 1,023

,415 medium batch size ,329 ,593 large batch size

  • ,152

,850 medium complex products

  • ,383

,532 complex products

  • ,248

,730 supplier company

  • 1,485

,004 main competition factor: price/cost ,574 ,310 Ln import quota of inputs

  • ,143

,468 Ln export quota of inputs 1,101 ,004 Ln share of unskilled workers ,137 ,439 I40-enabling-use-til-2013_level1 1,884 ,095 I40-enabling-use-til-2013_level2 1,932 ,076 I40-enabling-use-til-2013_level2 2,618 ,016 Constant

  • 8,946

,000

Source: Kinkel, S. and Jäger, A. (2017): Auslandsverlagerungen, Rückverlagerungen und Digitalisierungsverhalten in der deutschen Industrie. Trends und Auswirkungen für den Produktionsstandort Deutschland, Karlsruhe

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  • Technical key competences:

Software development of modular applications (apps) and IT-based platforms

Integration with the programming of machine and plant controls

IT security and user-oriented IT design

  • Non-technical competences:

Comprehensive understanding of customer problems and business models

Analysis of complex data and making sense as “smart data”

Interdisciplinary cooperation, particularly between engineers and IT specialists

  • Agile development approach, early experimentation and testing, positive culture of

error: "be brave and fail fast“

Key competences for the digital integration

Source: Kinkel et al. (2016): Digital-vernetztes Denken in der Produktion. Studie für die IMPULS-Stiftung des VDMA, Karlsruhe, November 2016

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Conclusions for Industrial and Innovation Policy

 The advantages of cost-based offshoring to LCC have clearly diminished, however

  • ffshoring intensity is still higher than backshoring intensity

 Positive correlation between the adoption of I4.0 enabling technologies and backshoring  limited with respect to jobs directly created at the home base, as “new production” is more automated  Indirect effects through local purchase of equipment and infrastructure as well as local sourcing of inputs and services  What policy can do  Support regional clusters and local value chains  Support local demand for innovative and more sustainable solutions (e.g. public procurement, “Made in” local value chains)  Support development and adoption (!) of smart and agile production systems (e.g. Industry 4.0, flexible and individualized manufacturing, additive manufacturing)  Support development of smart, data-driven services and business models for B2B  Support education, qualification and competence development of skilled personnel, limit bottlenecks

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  • Prof. Dr. Steffen Kinkel

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  • Prof. Dr. Steffen Kinkel

ILIN Institute for Learning and Innovation in Networks (www.ilin.eu) Karlsruhe University of Applied Sciences steffen.kinkel@hs-karlsruhe.de

Questions?

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  • Prof. Dr. Steffen Kinkel

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Target and source countries of

  • manuf. offshoring and backshoring

10% 49% 51% 13% 14% 16% 23% 27% 7% 32% 17% 9% 16% 4% 14% 0% 0% 9% 0% 0% 0% 13% 0% 54% 55% 40% 31% 30% 27% 21% 25% 16% 8% 11% 10% 7% 8% 9% 2% 2% 12% 2% 2% 3% 1% 0%

  • 60% -50% -40% -30% -20% -10%

0% 10% 20% 30% 40% 50% 60% EU 13 (2015) (2012) (2009) China (2015) (2012) (2009) Rest of Asia (2015) (2012) (2009) EU 15 (2015) (2012) (2009) North America (2015) (2012) (2009) Rest of Eastern Europe (2015) (2012) (2009) Latin America (2015) (2012) (2009) Rest of World (2015) (2012) (2009)

Source countries of Reshoring (n = 36 (2015) | 32 (2012) | 46 (2009)) Target countries for Offshoring (n = 128 (2015) | 154 (2012) | 161 (2009))

Source: European Manufacturing Survey 2015, Fraunhofer ISI

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Main motives for offshoring of production activities

Main motives for manufacturing relocations Manufacturing relocation mid 2004 to mid 2006 Manufacturing relocation 2007 to mid 2009 Manufacturing relocation 2010 to mid 2012 Manufacturing relocation 2013 to mid 2015 Trend Labour costs 80 % 77 % 71 % 75%  Access to new markets 27 % 28 % 28 % 27%  Vicinity to key customers 21 % 29 % 26 % 29%  Vicinity to to relocated production capacities n.a. 16 % 23 % 20%  Access to raw materials n.a. n.a. 15 % 12%  Import restrictions n.a. n.a. 11 % 9%  Lack of skilled workers n.a. 8 % 9 % 13% () Taxes, levies, subsidies 11% 12% 5% 11%  Following competition n.a. n.a. n.a. 10% n.a.

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Use of Industry 4.0 enabling technologies in German manufacturing industry

67% 11% 33% 19% 31% 30% 27% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Enterprise Resource Planning Systems (ERP) Product Lifecycle Management Systems (PLM) Digital visualization Mobile devices for programming / operation of machines Digital exchange of disposition data (SCM) Techniques for automation and control of internal logistics Real-time manufacturing execution system (MES) Source: German Manufacturing Survey 2015, Fraunhofer ISI Digital Management Systems Wireless human-machine communication CPS-related operations

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  • Prof. Dr. Steffen Kinkel

15 Cox & Snell: 0,077 Nagelkerkes: 0,168 Regression coefficient B Sig. Step 1 Ln #employees ,449 ,000 sec99_other manufacturing

  • ,435

,475 sec24_metal & metal components

  • ,325

,617 sec26_Data processing equipment, electronic and optical products

  • ,291

,706 sec27_electrical equipment 1,094 ,097 sec28_machinery & equipment ,159 ,808 medium batch size ,377 ,255 large batch size

  • ,349

,472 medium complex products ,039 ,917 complex products ,321 ,451 supplier company ,153 ,573 main competition factor: price/cost ,788 ,012 Ln import quota of inputs ,153 ,213 Ln export quota of inputs ,215 ,107 Ln share of unskilled workers ,121 ,271 I40-readyness-til-2013_level1 ,323 ,430 I40-readyness-til-2013_level2 ,463 ,223 I40-readyness-til-2013_level2 ,250 ,538 Constant

  • 6,437

,000

Logit model for offshoring propensity of companies

 Large firms are more active in

  • ffshoring

 Companies with a focused price/cost leadership strategy are more active in offshoring  No effects of Industry 4.0 readiness on

  • ffshoring

propensity

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  • Prof. Dr. Steffen Kinkel

16 Cox & Snell: 0,055 Nagelkerkes: 0,230 Regression coefficient B Sig. Step 1 Ln #employees ,072 ,673 sec99_other manufacturing

  • ,038

,974 sec24_metal & metal components

  • ,093

,938 sec26_Data processing equipment, electronic and optical products ,691 ,561 sec27_electrical equipment ,439 ,724 sec28_machinery & equipment

  • 1,023

,415 medium batch size ,329 ,593 large batch size

  • ,152

,850 medium complex products

  • ,383

,532 complex products

  • ,248

,730 supplier company

  • 1,485

,004 main competition factor: price/cost ,574 ,310 Ln import quota of inputs

  • ,143

,468 Ln export quota of inputs 1,101 ,004 Ln share of unskilled workers ,137 ,439 I40-enabling-use-til-2013_level1 1,884 ,095 I40-enabling-use-til-2013_level2 1,932 ,076 I40-enabling-use-til-2013_level2 2,618 ,016 Constant

  • 8,946

,000

Logit model for backshoring propensity of companies

 Company size no factor for backsho- ring propensity  Supplier companies are more reluctant to backshoring  Export-intensive firms are more active in back- shoring, to shorten their upstream value chains  Positive effects of use of Industry 4.0 enabling technologies on backshoring propensity