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Does Operational Excellence Influence Small and Mid-sized FirmISA2016Voytek

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Does Operational Excellence Influence Small and Mid-sized FirmISA2016Voytek

  1. 1. Does Operational Excellence Influence Small and Mid-sized Firm Performance? Kenneth P. Voytek Kenneth.Voytek@nist.gov Chief Economist Manufacturing Extension Partnership National Institute of Standards and Technology May 2016 Paper prepared for the 2016 Industry Studies Conference Minneapolis, MN
  2. 2. Research Question • Well known that firm performance varies considerably even when looking in the same industry (Syverson, 2011). Do these differences reflect idiosyncratic factors specific to a firm, its environment or are these differences related to more systematic factors that firms can control and change (Bloom, et al, 2013)? • Management matters, of course, but how? Moving beyond Bloom. • Is operational excellence (in terms of internal firm characteristics such as markets, management systems, strategy) associated with better performance? Does management matter to firm performance?
  3. 3. The Basic Model Internal Firm Factors (from Assessment) Both Internal dimensions and external market factors Firm Characteristics (controls) Revenue & Industry Hi/Low Performance (Profitability/Productivity (SPE))
  4. 4. Research Design • Cross sectional correlation design. • Basic descriptive statistics, cross-tabs, correlation, and logit models. • Controls include firm size (revenue) and industry (mfg./non-mfg.).
  5. 5. About the Data • Proprietary Data from CoreValue (N=1,456 cases: 350 Manufacturing Firms, 1,106 Non-manufacturing firms). Only have data for 500 cases with employment. Only 101 Manufacturing firms with employment. • Self assessment (sometimes guided) on 18 key dimensions of a firm including management systems (finance, operations, legal, HR, innovation), strategy, markets (by size, market share, barriers to entry, customers), products (differentiation, brand). Likert scale (0, 3, 5, 7, & 10). How close is a company to best practice (‘0’ means no alignment whatsoever with the best practice/standard, while a ‘10’ means they are in perfect alignment). • Performance Variables (self-reported) on Revenue, EBITDA and other information on Employment, Industry (mfg. or non-mfg.).
  6. 6. Basic Descriptive Statistics Revenue EBIDTA Profit Margin Employment Sales Per Employee # of Cases 1,456 1,456 1,456 500 500 Mean $11,041,312 $1,188,696 23.9% 50.7 $312,555 Std. Dev. $30,933,567 $3,590,956 71.2% 182.5 $796,167 Median $2,800,000 $300,000 11.7% 15.0 $150,000 • Smaller Firms • Typical firm has: • Just over $11M in revenue, • About 51 employees, • A profit margin of just under 24%, and, • About one-quarter of sample is in manufacturing. • Some errors in the data (revenue, EBITDA, employment). • Differences across the firms on self-assessment scores.
  7. 7. Data Analysis • Created groupings of firms into high and low performance categories based on median profit margin and sales per employee. Firms above median were coded as 1 (high performers) and firms below median were coded as zero (low performers). • Created a series of dummy variables based on self assessment across 17 dimensions. Firms with an assessment of >= 7 were coded as 1 (best practice) and all others were coded as 0. • Also compared a combined management score based on 17 dummy variables and average score (management score/17). • Compared best practice groupings using simple bivariate correlations and differences across high/low performance groupings. • Correlations indicated that variables such as growth, market size, customers, strategy, operations, and innovation were positive and significant related to better performance. Confirmed by other bivariate analysis (chi-square).
  8. 8. Differences in Best Practices Across Hi-Low Profit Margin Groups (N = 1,456) 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% High Margin Low Margin
  9. 9. Differences in Management Score and Average Management Score Across Profit Margin Groups 8.6 7.9 7.4 7.6 7.8 8 8.2 8.4 8.6 8.8 Management Score High Margin Low Margin 0.51 0.47 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 Average Management Score High Margin Low Margin
  10. 10. Differences in Best Practices Across Hi-Low Sales Per Employee Groups (N = 500) 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% Hi SPE Low SPE
  11. 11. Differences in Management Score and Average Management Score Across SPE Groups 8.4 8 7.8 7.9 8 8.1 8.2 8.3 8.4 8.5 Management Score High SPE Low SPE 0.49 0.47 0.46 0.465 0.47 0.475 0.48 0.485 0.49 0.495 Average Management Score High SPE Low SPE
  12. 12. The basic Logistic Regression Model • Dependent Variable: 1 = above median profit margin and 0 otherwise. Or SPE 1 = above median SPE and 0 otherwise • Independent Variables: 19 variables in the model.  Log of Revenue  Industry Dummy 1 = Manufacturing, 0 otherwise Dummy Variables: 1 = best practice, 0 otherwise  Growth Orientation  Market Size  Market Share  Recurring Revenue  Barriers  Product Differentiation  Brand  Customer Diversification  Strategy  Financial Systems  Marketing Systems  Operations  Customer Satisfaction  Management  Human Resources  Legal  Innovation
  13. 13. Logistic Results: Profit Margin Groups(N=1,456) B S.E. Wald df Sig. Exp(B) GROWTH .413 .124 11.143 1 .001 1.512 MKTSIZE .125 .121 1.075 1 .300 1.133 RECREVENUE .175 .129 1.837 1 .175 1.192 BARRIERS .091 .124 .532 1 .466 1.095 DIFFERENTIATION -.022 .131 .029 1 .864 .978 BRAND -.008 .130 .004 1 .952 .992 CUSTOMERDIV .214 .126 2.885 1 .089 1.238 STRATEGY .200 .118 2.857 1 .091 1.221 FINANCIALSYS -.116 .145 .641 1 .423 .891 MARKETINGSYS .107 .131 .668 1 .414 1.113 OPERATIONS .474 .141 11.337 1 .001 1.607 CUSTSAT -.055 .133 .168 1 .682 .947 MGMTSYS -.074 .136 .294 1 .587 .929 HRSYS -.142 .135 1.111 1 .292 .867 INNOVATION .107 .130 .668 1 .414 1.113 MARKETSHARE .094 .142 .435 1 .510 1.098 LEGALSYS -.081 .144 .317 1 .573 .922 Logrev -.388 .036 118.090 1 .000 .678 MfgDummy -.144 .139 1.076 1 .300 .866 Constant 5.060 .514 96.737 1 .000 157.540 67% of the cases are correctly predicted Cox & Snell R Square= .134 Nagelkerke R Square = .179
  14. 14. Odds Ratio: Profit Margin Predictors 1.607 1.512 1.238 1.221 1.133 0.678 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Operations Growth Customer Diversification Strategy Recurring Revenu Log Revenue (Size) Odds Ratio
  15. 15. Logistic Results: Sales Per Employee Groups (N=500)B S.E. Wald df Sig. Exp(B) GROWTH -.298 .234 1.623 1 .203 .743 MKTSIZE -.382 .225 2.884 1 .089 .683 RECREVENUE -.204 .254 .643 1 .423 .816 BARRIERS -.100 .236 .180 1 .671 .905 DIFFERENTIATION .465 .258 3.241 1 .072 1.591 BRAND .058 .248 .055 1 .815 1.060 CUSTOMERDIV .156 .235 .439 1 .507 1.169 STRATEGY .362 .224 2.601 1 .107 1.436 FINANCIALSYS -.415 .278 2.232 1 .135 .660 MARKETINGSYS .013 .245 .003 1 .958 1.013 OPERATIONS .610 .273 4.986 1 .026 1.841 CUSTSAT -.335 .251 1.785 1 .182 .715 MGMTSYS -.068 .256 .071 1 .790 .934 HRSYS .009 .255 .001 1 .972 1.009 INNOVATION -.010 .259 .002 1 .968 .990 MARKETSHARE -.621 .271 5.227 1 .022 .538 LEGALSYS -.238 .265 .804 1 .370 .788 Logrev .772 .085 83.415 1 .000 2.165 MfgDummy -.322 .285 1.283 1 .257 .724 Constant -10.725 1.206 79.110 1 .000 .000 72% of the cases are correctly predicted Cox & Snell R Square= .264 Nagelkerke R Square = .353
  16. 16. Odds Ratio: Sales Per Employee Predictors 2.165 1.841 1.607 1.436 0.715 0.683 0.538 0.678 0 0.5 1 1.5 2 2.5 Log Revenue (Size) Operations Differentiation Strategy Cust Sat MarketSize Market Share Financial Systems Odds Ratio
  17. 17. Conclusions • Performance varies considerably. • Industry does not seem to matter but a blunt measure. Some indication that manufacturing is related to below average performance but may reflect smaller N. • Size does matter. But, bigger is not always better. Size is negatively related to profit margin but positively related to sales per employee... • Management and Markets Matter. Strategy, growth orientation, operational performance, and diversified customer base are all likely to boost profit performance significantly. • Markets share and size is negatively related to above average sales per employee performance. Product differentiation, strategy, and operational performance boost sales per employee. • Aggregate Management Score and Avg Score are related to improved profit margin performance with controls. Mirror results above. Does not work well with Sales Per Employee. • More modeling. Subsets. Quantile Regression as another alternative. Other measures of performance. Better measure of productivity in particular. More sample. • Cleaning up the data. More detail. Other measures (more industry detail, ownership characteristics, etc.) • More data (limited N since employment was a new variable being collected). Look at changes over time.
  18. 18. References Bloom, et al. January 2013. Management in America. CES 13-01 Working Paper. http://www2.census.gov/ces/wp/2013/CES-WP-13-01.pdf Bloom, N. & Van Reenen, J. (2010b). Why do management practices differ across firms and countries? Journal of Economic Perspectives, 24(1), 203-224. Levinthal, D.A. (1997) Adaption on rugged landscapes. Management Science, 43(7), 934-950. March, J.G. & Sutton, R.I. (1997). Organizational performance as a dependent variable. Organization Science, 8(6), 698-706. Milgrom, P. & Roberts, J. (1990). The economics of modern manufacturing: Technology, strategy, and organization. American Economic Review, 80(3), 511-528. Roberts, J. (2004). The modern firm: Organizational design for performance and growth. New York: Oxford University Press. Syverson, C. (2014) The importance of measuring dispersion in firm-level outcomes. IZA World of Labor, 53, 1-10. http://wol.iza.org/articles/importance-of-measuring-dispersion-in-firm-level- outcomes-1.pdf Syverson, C. (2011). What determines productivity? Journal of Economic Literature, 49(2), 326-365.

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