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Manufacturing in Africa: Perspective from Bangladesh Ready Made Garment
R. Creedon, J. Krstic, R. Mann, K. Ruffini, M. Skuodis, K. Smula, M. Vlekke
Rocco Macchiavello
Warwick University
December 13th 2013
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Africans work in Small Organizations
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This seems to be bad for income per capita
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An Organizational Lens to Study Development
Development is about establishing large, well-run organizations
• Agriculture
• Manufacturing
• States/Government Agencies
• Military
Where does knowledge/ability to run large organization come from?
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Bangladesh Garments
Bangladesh Ready-Made Garment Sectors
• Second largest export of garments (after China)
• 3000+ (mostly) domestically owned factories,
• … employing 4 Million workers,
• Approx. 40% of all manufacturing jobs
Expected to rise further, given low wages and ample margins to increase productivity (far below international standards). Yet
• 75% increase in minimum wage
• Significant instability, and …
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… Rana Plaza
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… Rana Plaza
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Bangladesh Garments
Bangladesh Ready-Made Garment Sectors
• Second largest export of garments (after China)
• 3000+ (mostly) domestically owned factories,
• … employing 4 Million workers,
• Approx. 40% of all manufacturing jobs
Expected to rise further, given low wages and ample margins to increase productivity (far below international standards). Yet
• 75% increase in minimum wage
• Significant instability, and …
• … hence …
Many observers expect Africa’s role to increase in near future !
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Why Garments ?
Essentially every country that has ever industrialized started acquiring manufacturing experience in textile/garments.
We have focused on sewing sections of woven and light knit*. Why?
1. Simplest manufacturing process: the production line
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Why Garments ?
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Why Garments ?
Essentially every country that has ever industrialized started acquiring manufacturing experience in textile/garments.
We have focused on sewing sections of woven and light knit*. Why?
1. Simplest manufacturing process: the production line
2. Studying numerous production units with 50-100 workers
3. Good ways of measuring and benchmarking productivity
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Defining productivity
www.juko.com.pl
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Defining productivity
STYLE # TUEB-2368Print
EmbNo
BUYER 2157 SN 6 OPERATORS 15
COLOUR AOP 60% OL 6 HELPERS 1
Des Kids Basic Buttom 1300 FL 2 Total manpower 16
4.45 Others Initial date 2-Sep-12TGT/HR 1 Total 88 Working hour 10
130 Total 14 Revised date
TGT/HR 0 5
# Operation M/C EST. SMV ETS. TGT. Hour Req Req. Man Actual MC Actual helper Remark's
1 Join front rise OL 0.30 200 #DIV/0! 0.02 1
2 Join Back rise OL 0.30 200 0.0 0.02 1
3 Tack Care label SN 0.19 316 #DIV/0! 0.01 1
4 Join side seam OL 0.60 100 0.0 0.04 2
5 Join Inseam OL 0.56 107 0.0 0.04 2
6 Mark & cut elastic Helper 0.25 240 #DIV/0! 0.02 1
7 Tack Elastic & Mark SN 0.28 214 0.0 0.02 1
8 Attach elastic At waist OL 0.30 200 0.0 0.02 1
9 Main Label Tack SN 0.19 316 0.0 0.01 1
10 Fold & tack at waist SN 0.35 171 0.0 0.02 1
11 T/st At Waist FL 0.26 231 0.0 0.02 1
12 Hem bottom FL 0.28 214 0.0 0.02 1
13 Tack At Inseam & waist (3 Tacks) SN 0.30 200 0.0 0.02 1
14 Tack At Bottom hem ( 4 Tacks) SN 0.34 176 #DIV/0! 0.02 1
4.45 15 1
TGT/HR 4 TGT/HR
Sample Referance: Seal Sample.
TOTAL
Planned eff%
Planned Tgt/10Hrs
Total SMV
TGT/8HR 6TGT/HR
WORKSTUDY DEPARTMENTOPERATION BREAKDOWN SHEET
No Machine Summery Manpower Summery
TGT 100%
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Why Garments ?
Essentially every country that has ever industrialized started acquiring manufacturing experience in textile/garments.
We have focused on sewing sections of woven and light knit*. Why?
1. Simplest manufacturing process: the production line
2. Studying numerous production units with 50-100 workers
3. Good ways of measuring and benchmarking productivity
So what do we do? And what do we want to learn?
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What do we do?
Collect production data from several factories
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Measuring productivity: Sample of raw data
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Measuring productivity: Sample of raw data
One factory for one day… and other files on quality defects and absenteeism.
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What do we do?
Collect production data from several factories
Measure Productivity
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Measuring Productivity
Efficiency = Output minutes / input minutes
[# pieces * SMV] / [# operators * runtime in minutes]
Typically:
• 35- 40% in Bangladesh (best ~ 60 percent)
• 70 – 80% in China/Sri Lanka
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What do we do?
Collect production data from several factories
Measure Productivity
Understand Extent of (Persistent) Productivity Differences
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Dispersion: across and within 0
.02
.04
.06
.08
0 20 40 60 80Efficiency (Output Minutes / Input Minutes)
TFP Disp. (Across Factories) TFP Disp. (Within Factories)
Across factories:
75th / 25th: 1.95 ; 90th/10th = 2.79
Benchmark (Syverson 2004 – VA / Hrs):
75th / 25th = 1.92; 90th/10th = 4.02
Within factory (across lines)
75th / 25th = 1.22; 90th/10th = 1.64
Samples: Across: 5 factories with most homogenous
data; within:
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Persistence, across lines, within factories
711
710
715
697
720
719
704
702
701
720
715
698
705
718
28
708
70371920726209
71417
339
2518
2426
208337
707
709
24
703
339337
338211
338
207
340
21210
340208209
2134125
210
213212
211
716
28
717
17341
336
708
212
217
698
213
18217
335
22214
214
19
702
20
718
216
706
1920
706
23
717
22
215
23
707
216
711
27
215
27
697
336
705
335
699
710
701
700
700699
704
714
709
716
-20
-10
01
02
0
E(
Effic
ien
cy | X
)
-20 -10 0 10 20 30E( Efficiency (Lagged) | X )
coef = .270***, se = .073
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What do we do?
Collect production data from several factories
Measure Productivity
Understand Extent and source of (Persistent) Productivity Differences (PPDs)
In the process, evaluate training, consulting services • Female operators to supervisor (phase I, concluded; phase II planned)
• Existing supervisors: effects & demand (concluded)
• Consulting (planned)
Plan: collect same data from factories in other countries offering benchmarking tool
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Benchmarking Tool
Introduction
Select the time period for analysis
Select the month for analysis
Select the date for analysis.
Financial Metrics
A. Labor Cost per Earned Minute (Tk) B. Average Cost for Wasted Time (Tk)
0.9 0.6
1.5 1.0
2.3
-
-
3.4
-
1
1
2
2
3
3
4
4
5
5
Line-04 Line-05 Line-06 Line-07
Taka
Sewing Lines
OT
NH
3,063
1,049
5,467
191 -
1,000
2,000
3,000
4,000
5,000
6,000
Line-04 Line-05 Line-06 Line-07Ta
kaSewing Lines
Adequate firms capabilities
Benchmarking against other firms in same country/other country
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Garments in Africa
What are the pre-requirements to develop a vibrant export oriented garment sector?
1. Favorable trade policy (AGOA, MFA*)
2. Access to inputs (cotton, fabric*)
3. Access to finance:
- Set up costs low in Cut Make Trim (CMT)
- Back to Back L/C
4. Infrastructure (Roads and Ports)
5. Electricity
6. Skilled labor force
- Operators (mostly women, learn fast)
- Line Supervisors/Line Chiefs: this is the challenge.
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Garments in Africa I
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Garments in Africa II
What now?
Ethiopia
Kenya
Tanzania
Ghana
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Back to Big Picture:
Where does knowledge/ability to run large organizations come from?
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Back to Big Picture:
Where does knowledge/ability to run large organizations come from?
Good Management
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Do Line Supervisors matter ?
Operators are moved with some frequency across lines, esp. when there are large numbers of absent workers or style changes
Supervisors are move much less frequently. (~7% moved over 4 months in sample from another project.)
But, Do supervisors matter? First, look at what workers say are the most important tasks of supervisors:
Characteristics were placed in overlapping groups of three, and respondents were asked to rank
them. Scored 3 if the characteristic was ranked first, 2 if second, 1 if third.
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SV characteristics and productivity: tenure
641 500
271
693
669
698
463
463
94691
739
287877
877
464
464
536480
793
668
482
267
908
788
943
246
459
459
238
734
283
381381416
486
383
383
92
796
785
869
869
285
671
385
385418
286
728
261696
948757
421
386
386384
384
875
875
242
873
873
237
876
876
920
470
566
272
472
553
644
582
460
460461
461
759
87
282
781
642
264940
665
694
-10
-50
51
0
E(
effic
iency | X
)
-10 -5 0 5 10E( tenure | X )
coef = -0.500***, se = 0.159
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SV characteristics and productivity: gender
668
876
876
873
873
940
416
582
759
383
383
877
877
381381
948
418
728739385
385
460
460
461
461
943
734
264
696
781
92
282
694
271
482
285
644
23787
472
642
920
238
553
287
785
566
464
464386
386
242
486
470272
261
796286
908
788
793
693
246
480641
665
536
698
91
463
463500283
267
459
459
671
669
875
875
421
869
869
384
384
946757
-10
-50
51
0
E(
Effic
ien
cy | X
)
-.5 0 .5 1E( Female Supervisor | X )
coef = 2.303*, se = 1.274
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Back to Big Picture:
Where does knowledge/ability to run large organization come from?
Good Management
OK. But what do we mean by “good management”?
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Trust
TurkeyPortugal
France
Argentina
Spain
Brazil
Austria
Belgium
India
Italy
South Korea
ChileGermany
UK
Japan
Canada
Ireland
Switzerland
Mexico
South Africa
Netherlands
US
Denmark
Finland
Norway
Sweden
-.2
-.1
0.1
.2.3
Sale
s L
arg
e F
irm
s/G
NP
-.2 -.1 0 .1 .2Trust
B = 0.75***, Controls: Gini Coefficient
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Common Understanding
1. Trust is key to well functioning relationships
2. A “common understanding” is key for trust (Gibbons and Henderson 2012)
3. Measurement (1-2 SVs + two randomly selected operators): • Now I am going tell you some things about the factory. Please tell me whether
you totally agree, somewhat agree, somewhat disagree or totally disagree.
It is OK if a worker doesn't come to work without giving any prior notice because his/her child is sick
It is OK if a worker doesn't come to work without giving any prior notice because of heavy rain
It is OK if a worker doesn't come to work without giving any prior notice because her sister is getting married.
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“common understanding” and productivity
472
788
480
796948
869
869283
416
734
759
386
386
671
940
482
566
946
282
553
669
383
383728
421793
873
873
286
536
384
384
668582
696
92
644877
877
264
757
876
876
385
385
694
287267
459
459
237
261
460
460
271
461
461
641
246
920
23890891
463
463698
242
464
464
87
642
693272 418
486
785
781470
665
875
875
381381
500
285
943
739
-10
-50
51
0
E(
Effic
ien
cy | X
)
-.5 0 .5 1E(Agreement with Operators | X )
coef = 2.464*, se = 1.405
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So What?
• Are these ideas relevant elsewhere ? In Africa ?
Coffee Washing Stations in Rwanda
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Trust and Efficiency of CWS in Rwanda
77.5
8
1.5 2 2.5 3Local Trust
Unit Cost (Log) Fitted values
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Trust and Farmer’s Price share of Unit Costs
.4.5
.6.7
.8
1.5 2 2.5 3Local Trust
Cherries, Share of Unit Cost Fitted values
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Next Steps
1. Leverage connections with buyers (H&M, Tesco, Sainsbury), IGC and our team expertise in collecting and analyzing production data to benchmark African’s capabilities in the garment sector.
Ethiopia
2. Better understand the role of “trust” in shaping firms’ organizational choices and – ultimately – productivity in a variety of contexts in Africa
Where does trust come from? Which organizational practices promote it? Training? Information Systems?
If interested, please get in touch: [email protected]