The Manufacturing Quality Implications of Collocating R&D and Manufacturing

1. The Manufacturing Quality Implications of Collocating R&D and Manufacturing John Gray The Ohio State University EnnoSiemsen University of Minnesota Gurneeta Vasudeva University of Minnesota

2. 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? Manufacturing Plant, Bristol Myers Squibb, New Brunswick NJ Collocated Plant, Bristol Myers Squibb, Syracuse NY R&D Site, Bristol Myers Squibb, Princeton NJ

3. Hypotheses

4. First-Order Effect of Collocation: Competing Hypotheses H1a vs. H1b

5. Problem Solving Activities Development Ramp-Up Full-Scale Production Product Lifecycle • Quality Improvement • Troubleshooting • Supplier/Engineering Changes • Creating a Manufacturable Design • Rapid Prototyping • Parallel Process Development • Global Search • Prototype -> Product • Robust Scaling • Know Why Transfer Manufacturing Involvement R&D Involvement

6. 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)

7. LogicH1a

8. The Drawbacks of Collocation More complexity Cognitive overload Less Specialization Diverging incentives Managerial inattention 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)

9. 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)

10. LogicH1b

11. 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

12. Data

13. Databases

14. Measures • Dependent Variable • FDA District Decision Inspection Outcomes (1994-2007) • Independent Variables • Collocation (Delphion Patents) • Company Size (Compustat) • Industry Classification (ORBIS)

15. 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)

16. Breadth vs. Depth Original FDA Dataset 30,000 Inspections in 14,000 sites Cleaned FDA Dataset 8,800 Inspections in 1,250 plants Final Dataset 2,304 Inspections in 292 plants

17. Analysis

18. Model • Random effects ordered profit • Two levels: Inspection and Plant • Estimated using Stata’s GLLAM

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

20. Results(control variables omitted) H1a supported; H1b “rejected” Notes: Higher numbers indicate WORSE conformance quality performance **p<.01, *p<.05, †p<.10 (two-tailed) Omitted firm size is “Small”; Omitted Industry is “Pharmaceutical Preparations” (more complex)

21. Results(control variables omitted) H2 supported Notes: Higher numbers indicate WORSE conformance quality performance **p<.01, *p<.05, †p<.10 (two-tailed) Omitted firm size is “Small”; Omitted Industry is “Pharmaceutical Preparations” (more complex)

22. Results(control variables omitted) H3 Supported Notes: Higher numbers indicate WORSE conformance quality performance **p<.01, *p<.05, †p<.10 (two-tailed) Omitted firm size is “Small”; Omitted Industry is “Pharmaceutical Preparations” (more complex)

23. The Effect of Collocation

24. The Effect of Collocation

25. 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

26. Conclusion

27. Summary of Findings • Collocated plants have increased manufacturing quality performance • if they belong to larger companies, • that use more complex manufacturing processes.

28. 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

29. 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?

30. Thank You