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

Africans work in Small Organizations

This seems to be bad for income per capita

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?

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 …

… Rana Plaza

… Rana Plaza

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 !

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

Why Garments ?

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

Defining productivity

www.juko.com.pl

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%

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?

What do we do?

Collect production data from several factories

Measuring productivity: Sample of raw data

Measuring productivity: Sample of raw data

One factory for one day… and other files on quality defects and absenteeism.

What do we do?

Collect production data from several factories

Measure Productivity

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

What do we do?

Collect production data from several factories

Measure Productivity

Understand Extent of (Persistent) Productivity Differences

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:

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

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

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

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.

Garments in Africa I

Garments in Africa II

What now?

Ethiopia

Kenya

Tanzania

Ghana

Back to Big Picture:

Where does knowledge/ability to run large organizations come from?

Back to Big Picture:

Where does knowledge/ability to run large organizations come from?

Good Management

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.

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

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

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”?

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

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.

“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

So What?

• Are these ideas relevant elsewhere ? In Africa ?

Coffee Washing Stations in Rwanda

Trust and Efficiency of CWS in Rwanda

77.5

8

1.5 2 2.5 3Local Trust

Unit Cost (Log) Fitted values

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

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: r.macchiavello@warwick.ac.uk