the manufacturing quality implications of collocating r&d and manufacturing
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The Manufacturing Quality Implications of Collocating R&D and Manufacturing. John Gray The Ohio State University Enno Siemsen University of Minnesota Gurneeta Vasudeva University of Minnesota. Collocating Manufacturing and R&D. - PowerPoint PPT PresentationTRANSCRIPT
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The Manufacturing Quality Implications of Collocating R&D
and Manufacturing
John Gray The Ohio State UniversityEnno Siemsen University of MinnesotaGurneeta Vasudeva University of Minnesota
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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
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HYPOTHESES
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First-Order Effect of Collocation: Competing Hypotheses
H1a vs. H1b
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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
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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)
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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)
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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
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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)
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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)
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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
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DATA
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Databases
FDA Establishment Inspections
Delphion Patent Database
COMPUSTAT
ORBIS
Census
National Establishment Time Series
Thomson, Google
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Measures
• Dependent Variable– FDA District Decision Inspection Outcomes
(1994-2007)• Independent Variables
– Collocation (Delphion Patents)– Company Size (Compustat)– Industry Classification (ORBIS)
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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)
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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
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ANALYSIS
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Model
• Random effects ordered profit– Two levels: Inspection and Plant
• Estimated using Stata’s GLLAM
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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)
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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”
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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
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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
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The Effect of Collocation
Large F
irm, N
on C
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Basic P
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on-C
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Basic P
harm
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cals
0%10%20%30%40%50%60%70%80%90%
100%
36%57% 49% 46%
46%
36% 40% 42%
18% 7% 11% 12%
Official ActionVoluntary ActionNo Action
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The Effect of Collocation
Large F
irm, N
on C
olloc
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Pharm
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cal Prep
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Large F
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olloc
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Pharm
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Large F
irm, C
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Basic P
harm
aceuti
cals
Large F
irm, N
on-C
olloc
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Basic P
harm
aceuti
cals
0%10%20%30%40%50%60%70%80%90%
100%
36%57% 49% 46%
46%
36% 40% 42%
18% 7% 11% 12%
Official ActionVoluntary ActionNo Action
Lik
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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
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CONCLUSION
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Summary of Findings
• Collocated plants have increased manufacturing quality performance– if they belong to larger companies,– that use more complex manufacturing
processes.
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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
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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?
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Thank You