nick bloom and john van reenen, management practices, 2010 key messages of lectures 1 to 4 1.exists...

Nick Bloom and John Van Reenen, Management Practices, 2010

Key messages of lectures 1 to 4

1. Exists a set of core practices for talent management, target management and performance management (scoring grid)

2. Associated with better performance across a wide range of countries and industries, especially in larger firms

3. Not universal truths, but important benchmarks against which all firms should be evaluated

4. Firms are often unaware that their practices are lacking, so good management is similar to a new technology

5. Hard to change practices in firms – anecdotal evidence this takes several years

Nick Bloom and John Van Reenen, Management Practices, 2010 2

Improving management in Indian factories

Nick Bloom (Stanford Economics)John Van Reenen (Stanford GSB/LSE)

Lecture 5


2.6 2.8 3 3.2 3.4mean of management

USGermanySweden

JapanCanadaFrance

ItalyGreat Britain

AustraliaNorthern Ireland

PolandRepublic of Ireland

PortugalBrazilIndia

ChinaGreece

3

Management appears worse in developing countries

Average Country Management Score, firms 100 to 5000 employees(from Bloom & Van Reenen (2007, QJE), Bloom, Sadun & Van Reenen (2009, AR))

69533627012234431218876238292231102140

524171

620559

# firms

Nick Bloom and John Van Reenen, Management Practices, 2010 4Firm-Level Management Scores

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

US manufacturing, mean=3.33 (N=695)

Indian manufacturing, mean=2.69 (N=620)

India’s low score is mainly due to many badly managed firmsD

en

sity

De

nsi

ty


This raises two obvious questions

1. Does “bad” management reduce productivity, or are these practices dues to difference circumstances in India (i.e. poor infrastructure, less capital, weak rule of law)?

2. If it does matter, why are so many Indian firms badly managed?


Summary and photos

Experiment on plants in large (≈ 300 person) Indian textile firms

Randomized treatment plants get heavy management consulting, control plants get very light consulting (just enough to get data)

Collect weekly performance data on all plants from 2008 to 2010

• Improved management practices led to large and significant improvements in productivity and profitability

• Appears informational constraints were a major reason for lack of prior adoption, but often other constraints also present

Before explaining research and results in detail, I want to show some slides to provide some background


Exhibit 1: Plants are large compounds, often containing several buildings.

Plant surrounded by grounds

Front entrance to the main building Plant buildings with gates and guard post

Plant entrance with gates and a guard post


Exhibit 2: These plants operate 24 hours a day for 7 days a week producing fabric from yarn, with 4 main stages of production

(1) Winding the yarn thread onto the warp beam (2) Drawing the warp beam ready for weaving

(3) Weaving the fabric on the weaving loom (4) Quality checking and repair


This production technology has not changed much over time:Lowell Mill warping looms (1854, Lowell, Massachusetts)

Warp beam

Krill


Exhibit 3: Many parts of these plants were dirty and unsafe

Garbage outside the plant Garbage inside a plant

Chemicals without any coveringFlammable garbage in a plant


Exhibit 4: The plant floors were disorganized

Instrument not

removed after use, blocking hallway.

Tools left on the floor after use

Dirty and poorly

maintained machines

Old warp beam, chairs and a desk

obstructing the plant floor


Yarn piled up so high and deep that access to back

sacks is almost impossible

Exhibit 5: The inventory rooms had months of excess yarn, often without any formal storage system or protection from damp or crushing

Different types and colors of

yarn lying mixed

Yarn without labeling, order or damp protection

A crushed yarn cone, which is unusable as it leads to

irregular yarn tension


Exhibit 6: Yet more material was often stored around the plant

Inventory was also regularly

stored in corridors, hallways,

doorways and on stairs. This is

dangerous and impedes efficient

movement of materials around

the plant.

Inventory was also often stored around

machinery.


No protection to prevent damage and rustSpares without any labeling or order

Exhibit 7: The parts stores were also disorganized and dirty

Shelves overfilled and disorganizedSpares without any labeling or order


Exhibit 8: The path for materials flow was often obstructed

Unfinished rough path along which several 0.6 ton warp beams were taken on wheeled trolleys every day to the elevator, which led down to the looms.

This steep slope, rough surface and sharp angle meant workers often lost control of the trolleys. They

crashed into the iron beam or wall, breaking the trolleys. So now each beam is carried by 6 men.

A broken trolley (the wheel snapped off)

At another plant both warp beam elevators had broken down due to poor maintenance. As a result teams of 7 men carried several warps beams down the stairs every day. At 0.6 tons each this was slow and dangerous - two serious accidents occurred in

our time at the plant.


Exhibit 9: Routine maintenance was usually not carried out, with repairs only undertaken when breakdowns arose, leading to frequent stoppages.

Parts being cleaned and replaced on jammed loomBroken machine parts being repaired

Loom parts being disassembled for diagnosisWorkers investigating a broken loom

Nick Bloom and John Van Reenen, Management Practices, 20100.2

.4

.6

.8

De

nsity

1 2 3 4 5management

17Management scores

Brazil and China Manufacturing,

mean=2.67

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

0.2

.4

.6

.8

1

De

nsity

1 2 3 4 5management

0.5

11

.5

De

nsity

1 3 5management

Indian Manufacturing,

mean=2.69

Indian Textiles, mean=2.60

Experimental Firms, mean=2.60

These firms appear typical of large manufacturers in India, China and Brazil


So ran an experiment to evaluate impact of changing the management of large Indian firms

• Obtained details of the population of 529 woven cotton fabric firms (SIC 2211) near Mumbai with 100 to 5000 employees.

• Selected 66 firms in the largest cluster (Tarapur & Urmagaon)

• Contacted every firm: 17 willing to participate in straight-away, so randomly picked 20 plants from these 17 firms

• A team of 6 consultants from Accenture, Mumbai was hired to help improve the practices in some of these firms• Control: 1 month of diagnostic• Treatment: 1 month diagnostic + 4 months implementation• All: follow-on data collection for next 12+ months

• Collecting data from April 2008 to December 2010


Sample of firms we worked with


Our plants and firms are large by Indian & US standards

Source: Hsieh and Klenow, 2009

Average size of our plants


Management practices before and after treatment

Performance of the plants before and after treatment

• Quality

• Inventory

• Operational efficiency

Why were these practices not introduced before?


Intervention aimed to improve 38 core textile management practices in 6 areas (1/2)

Targeted

practices in 6

areas:

operations,

quality,

inventory,

loom planning,

HR and sales

& orders


Intervention aimed to improve 38 core textile management practices in 6 areas (2/2)

Targeted

practices in 6

areas:

operations,

quality,

inventory,

loom planning,

HR and sales

& orders


.2.3

.4.5

.6

2008.25 2008.5 2008.75 2009 2009.25 2009.5 2009.75ym

Adoption of these 38 management practices did rise, and particularly in the treatment plants

Notes: Non-experiment plants are other plants in the treatment firms not involved in the experiment. They improved practices over this period because the firm internally copied these over themselves. All initial differences not statistically significant (Table 2)

Wave 1 treatment plants: Diagnostic September 2008, implementation began October 2008

Control plants:Diagnostic July 2009

Wave 2 treatment plants: Diagnostic April 2009, implementation began May 2008

Non-experiment plants:No intervention

January 2009 April 2009 July 2009October 2008July 2008 October 2009April 2008

Sha

re o

f the

38

man

agem

ent p

ract

ices

ado

pted


Take away summary points

1. These firms are not adopting basic management practices, in large part due to a lack of awareness

2. Changing practices is very slow – we are still introducing new practices into firms 18 months later, because of:

a. Takes time for firms to advice (Accenture in our case)

b. Changes are complementary – e.g. monitoring & pay

3. Change may not be permanent – need to fix both processes and incentives to avoid backsliding



Performance of the plants before and after treatment

• Quality

• Inventory




27

Exhibit 10: Quality was so poor that 19% of manpower was spent on repairing defects at the end of the production process

Workers spread cloth over lighted plates to spot defectsLarge room full of repair workers (the day shift)

Non-fixable defects lead to discounts of up to 75%Defects are repaired by hand or cut out from cloth


Previously mending was recorded only to cross-check against customers’ claims for rebates

Defects log with defects not recorded in an standardized format. These defects were recorded solely as a record in case of customer complaints. The data was not aggregated or analyzed


Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver


The quality data is now collated and analyzed as part of the new daily production meetings

Plant managers now meet

regularly with heads of

quality, inventory, weaving,

maintenance, warping etc.

to analyze data


40

60

80

10

01

20

14

0

-10 0 10 20 30timing

31

Defect rates have rapidly fallen in treatment plants

Notes: Displays the average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This is plotted for the 14 treatment plants (square symbols) and the 6 control plants (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention.

Control plants

Treatment plants

Weeks after the start of the intervention (diagnostic phase)

Diagnostic start Implementation start

Qua

lity

defe

cts

inde

x

Implementation stop


Management impact on quality, regressions



Performance of the firms before and after treatment

• Quality

• Inventory




Stock is organized, labeled, and entered

into an Electronic Resource Planning (ERP) system which

has details of the type, age and location.

Bagging and racking yarn reduces waste

from rotting (keeps the yarn dry) and crushing

Computerized inventory systems

help to reduce stock levels.

Organizing and racking inventory enables firms to reduce capital stock and reduces waste


Sales are also informed about excess yarn stock so they can incorporate this in new designs.

Shade cards now produced for all

surplus yarn. These are sent to the

design team to use in future designs


And yarn for products ranges no longer made by the firm (e.g. suiting fabric) was sold

This firms

used to make

suiting and

shirting yarn,

but stopped

making

suiting yarn 2

years ago


80

90

10

011

012

013

0

-40 -20 0 20timing

Inventory is falling in treatment firms

Control firms

Treatment firms

Weeks after the start of the intervention

Diagnostic Implementation

Notes: Displays the average raw materials for the 14 treatment firms (square symbols) and the 6 control firms (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention.


Management impact on inventory, regressions


Spare parts were also organized, reducing downtime (parts can be found quickly), capital stock and waste

Nuts & bolts sorted as per specifications

Tool

storage organized

Parts like gears,

bushes, sorted as per specifications




• Quality

• Inventory




The treated firms have also started to introduce basic initiatives (called “5S”) to organize the plant

Worker involved in 5S initiative on the shop floor, marking out the area

around the model machine

Snag tagging to identify the abnormalities on & around the machines, such as

redundant materials, broken equipment, or accident areas. The operator and the maintenance team is responsible for

removing these abnormalities.

This is all part of the routine maintenance


Production data is now collected in a standardized format, for discussion in the daily meetings

Before(not standardized, on loose

pieces of paper)

After (standardized, so easy to enter

daily into a computer)


Daily performance boards have also been put up, with incentive pay for employees based on this


Management impact on efficiency, regressions


Estimated impacts on productivity and profitability are large and rising

Estimate the intervention has increase profits by about $250,00 per firm and productivity by 9% so far from:- reduced repair manpower costs- reduced wasted materials (from less defects)- lower inventory- higher efficiency levels

Full impacts of better management should be much larger:- short-run impacts only- narrow set of management practices (almost no HR)




• Quality

• Inventory




So why did these firms have bad management?

• Information: management is a technology and India is far behind the technology frontier, e.g. Lean manufacturing

• Incentives: managers have no incentive pay or within firm promotion possibilities so have limited motivated to perform

• CEO ability: family firms with directors who struggled to change practices and sometimes procrastinated


Why does competition not fix badly managed firms?

Bankruptcy is still avoided : wage of $5 a day means firms are profitable

Reallocation appears limited: Owners take all decisions as they worry about managers stealing. But owners time is constrained – they current work 72.5 hours average a week – limiting growth.

As an illustration firm size is more linked to number of male family members (corr=0.689) - who are trusted to be given managerial positions - than management scores (corr=0.223)

Entry appears limited: Production is very capital intensive ($13m assets average per firm)


Summary

Firms in developing countries seem badly managed

Our results suggest this has a material impact on productivity

Also appear to find bad operations management arises from

lack of information and poor HR management

But far from clear….yields as many questions as answers so far


Back-up


40

60

80

10

01

20

14

0

-10 -5 0 5 10 15 20timing

Cubic Spline

Spline - 2 SE

Figure 3: Quality defects index for the treatment and control plants

Notes: Displays the average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This is plotted for the 14 treatment plants (♦ symbols) and the 6 control plants (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. “Data” is plotted using a 5 week moving average. To obtain series (rather than point-wise) confidence intervals we used a cubic-spline with one knot at the start of the implementation period. The spline estimate is labeled (“Cubic Spine”), the 95% confidence intervals labeled (“Spline + 2SE”) and (“Spline – 2SE”) from plant-wise block boostrap. Timing based on weeks after the intervention (positive values) or before the intervention (negative values). For wave 1 treatment plants this is relative to September 1st 2008, for Wave 2 treatment and control firms April 7th 2009. The control group’s rise in weeks 10+ are due to the pre Diwali and Ede production increase, which usually leads to a deterioration in quality due to increased speeds of production.

Control plants

Treatment plants

Weeks after the start of the intervention

Qu

alit

y d

efe

cts

ind

ex

(hig

he

r sc

ore

=lo

we

r q

ua

lity)

Start of Diagnostic Start of Implementation

Data (♦ symbol)

Spline + 2 SE

Data (+ symbol)

Cubic Spline

Spline - 2 SE

Spline + 2 SE


We work in Tarapur because textile mills no longer exist in Mumbai

The textile factories in downtown

Mumbai are now all closed as land

prices are too high. The last few

remaining building are now being

demolished and turned into

apartment blocks and shopping

malls

Apartment blocks

being built on the

site of an

demolished old

textile mill, on the

opposite side of

the road from the

one being

demolished

picture above

nick bloom and john van reenen, management practices, 2010 key messages of lectures 1 to 4 1.exists...

Documents

bloom van reenen

improved management

target management

talent management

good management

bad management

sadun van reenen

heavy management consulting