the manufacturing quality implications of collocating r&d and manufacturing

The Manufacturing Quality Implications of Collocating R&D

and Manufacturing

John Gray The Ohio State UniversityEnno Siemsen University of MinnesotaGurneeta Vasudeva University of Minnesota

Collocating Manufacturing and R&D

• Are there inherent on-going manufacturing performance advantages to collocation with R&D?

• Alternatively, are there disadvantages to manufacturing due to less “focus”?

• What are key moderators to any relationship between R&D-manufacturing collocation and manufacturing performance?

Collocated Plant, Bristol Myers Squibb, Syracuse NY

Manufacturing Plant, Bristol Myers Squibb, New Brunswick NJ

R&D Site, Bristol Myers Squibb, Princeton NJ

HYPOTHESES

First-Order Effect of Collocation: Competing Hypotheses

H1a vs. H1b

Manufacturing Involvement

Problem Solving Activities

Product Lifecycle

Development Ramp-Up Full-Scale Production

R&D Involvement

• Creating a Manufacturable Design• Rapid Prototyping• Parallel Process Development• Global Search

• Prototype -> Product• Robust Scaling• Know Why Transfer

• Quality Improvement• Troubleshooting• Supplier/Engineering Changes

Concurrent Engineering

R&D Manu-facturing

“Nowhere in a company is the need for coordination more acute than between the people who are responsible for product design and those responsible for manufacturing.”

(Dean and Susman 1989)

LogicH1a

Manufacturing and R&D are

interdependent throughout the

life cycle

There are benefits from

integrating these activities

Distance reduces integration;

Collocation is one Integration Mechanism

Collocated plants have better

manufacturing quality

performance than non-collocated plants (H1a)

The Drawbacks of Collocation

• A site is focused “to the extent that it limits the set of conflicting (…) activities in which workers and managers are engaged.”

(Huckman and Zinner 2008)

More complexity

Managerial inattention

Diverging incentivesLess Specialization

Cognitive overload

Focus

R&D Manu-facturing

“The essence of effective production management is stability, efficiency, discipline and tight control, whereas effective R&D management requires dynamism, flexibility, creativity, and loose control.”

(Clark and Fujimoto1999)

LogicH1bManufacturing and R&D are different activities, with different personnel,

objectives, etc.

Collocating such activities may hinder manufacturing’s focus on its main task

Loss of focus can hinder performance

Collocated plants have worse manufacturing quality performance than non-collocated plants (H1b)

Interaction Effects• (H2) Collocation is more beneficial for large companies

– Dynamic capabilities to manage subtle challenges of collocated plants

• Larger pool of professional managers• More experience in managing unfocused operations

• (H3) Collocation is more beneficial for more complex processes– Low complexity means little interdependence– Less tacit knowledge involved

DATA

Databases

FDA Establishment Inspections

Delphion Patent Database

COMPUSTAT

ORBIS

Census

National Establishment Time Series

Thomson, Google

Measures

• Dependent Variable– FDA District Decision Inspection Outcomes

(1994-2007)• Independent Variables

– Collocation (Delphion Patents)– Company Size (Compustat)– Industry Classification (ORBIS)

Control Variables• Inspection Level, e.g.

– Inspection type– Previous inspection outcome– Time since last inspection (Anand, Gray, & Siemsen 2011)

• Plant Level, e.g.– Plant Type (NETS)– Population Density (Census)– Plant Age (NETS + Search)– Cluster (FDA + geospatial plot)

• Company Level, e.g.– R&D Intensity (Compustat)– Capital Intensity (Compustat)– Inventory Turns (Compustat)– Tobin’s Q (Compustat)

Breadth vs. Depth

Original FDA Dataset

30,000 Inspections in 14,000 sites

Cleaned FDA Dataset

8,800 Inspections in 1,250 plants

Final Dataset2,304 Inspections in

292 plants

ANALYSIS

Model

• Random effects ordered profit– Two levels: Inspection and Plant

• Estimated using Stata’s GLLAM

Results(control variables omitted)

Variable Model 1 Model 2 Model 3Firm Size: Medium .12† .22** .23**

Firm Size: Large .03 .17 .21†

Industry: Basic Pharm -.14 -.15 -.32**

Industry: Other -.21 -.20† -.29†

Collocated -.14* .05 .00

Collocated*Med -.29* -.32*

Collocated*Large -.38* -.53*

Collocated*Basic Pharm .60**

Collocated*Other .28

Notes: Higher numbers indicate WORSE conformance quality performance**p<.01, *p<.05, †p<.10 (two-tailed)Omitted firm size is “Small”; Omitted Industry is “PharmaceuticalPreparations” (more complex)

Results(control variables omitted)

Variable Model 1 Model 2 Model 3Firm Size: Medium .12† .22** .23**

Firm Size: Large .03 .17 .21†

Industry: Basic Pharm -.14 -.15 -.32**

Industry: Other -.21 -.20† -.29†

Collocated -.14* .05 .00

Collocated*Med -.29* -.32*

Collocated*Large -.38* -.53*

Collocated*Basic Pharm .60**

Collocated*Other .28

Notes: Higher numbers indicate WORSE conformance quality performance**p<.01, *p<.05, †p<.10 (two-tailed)Omitted firm size is “Small”; Omitted Industry is “PharmaceuticalPreparations” (more complex)

H1a supported; H1b “rejected”

Results(control variables omitted)

Variable Model 1 Model 2 Model 3Firm Size: Medium .12† .22** .23**

Firm Size: Large .03 .17 .21†

Industry: Basic Pharm -.14 -.15 -.32**

Industry: Other -.21 -.20† -.29†

Collocated -.14* .05 .00

Collocated*Med -.29* -.32*

Collocated*Large -.38* -.53*

Collocated*Basic Pharm .60**

Collocated*Other .28

Notes: Higher numbers indicate WORSE conformance quality performance**p<.01, *p<.05, †p<.10 (two-tailed)Omitted firm size is “Small”; Omitted Industry is “PharmaceuticalPreparations” (more complex)

H2 supported

Results(control variables omitted)

Variable Model 1 Model 2 Model 3Firm Size: Medium .12† .22** .23**

Firm Size: Large .03 .17 .21†

Industry: Basic Pharm -.14 -.15 -.32**

Industry: Other -.21 -.20† -.29†

Collocated -.14* .05 .00

Collocated*Med -.29* -.32*

Collocated*Large -.38* -.53*

Collocated*Basic Pharm .60**

Collocated*Other .28

Notes: Higher numbers indicate WORSE conformance quality performance**p<.01, *p<.05, †p<.10 (two-tailed)Omitted firm size is “Small”; Omitted Industry is “PharmaceuticalPreparations” (more complex)

H3 Supported

The Effect of Collocation

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Robustness Tests

• Ordered Probit with Clustered Errors• Product/Process Patents• Geographical Sub-Clusters• Without Plant Age (Increased Sample)• Private Firms (3200 inspections in 577 plants)

– Results don’t generalize– Possibly because private firms are small

• Instrumental Variables Analysis– Stata CMP

CONCLUSION

Summary of Findings

• Collocated plants have increased manufacturing quality performance– if they belong to larger companies,– that use more complex manufacturing

processes.

Contributions

• Collocation as an integration mechanism• Manufacturing benefits of manufacturing

and R&D integration– On-going conformance quality performance

• Establishing key contingencies of collocation’s benefits to manufacturing

Future Work

• Study the effect of time– Has the importance of distance diminished?

• Communication technologies, standards

• Study mechanisms to balance collocation benefits and drawbacks

• Study in other settings– Healthcare: Teaching vs. Non-Teaching

Hospitals?

Thank You