Does Operational Excellence Influence Small and Mid-sized FirmISA2016Voytek
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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. 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. 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.
