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Key messages of lectures 1 to 4. Exists a set of core practices for talent management, target management and performance management (scoring grid) Associated with better performance across a wide range of countries and industries, especially in larger firms

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Key messages of lectures 1 to 4

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Key messages of lectures 1 to 4 l.jpg

Key messages of lectures 1 to 4

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

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

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

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

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


Improving management in indian factories l.jpg

Improving management in Indian factories

Nick Bloom (Stanford Economics)

John Van Reenen (Stanford GSB/LSE)

Lecture 5

2


Slide3 l.jpg

Management appears worse in developing countries

# firms

695

336

270

122

344

312

188

762

382

92

231

102

140

559

620

524

171

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


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

US manufacturing, mean=3.33 (N=695)

Density

Indian manufacturing, mean=2.69 (N=620)

Density

Firm-Level Management Scores


This raises two obvious questions l.jpg

This raises two obvious questions

  • 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)?

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


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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 l.jpg

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

Plant entrance with gates and a guard post

Plant surrounded by grounds

Front entrance to the main building

Plant buildings with gates and guard post


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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


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

Krill

Warp beam


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

Garbage outside the plant

Garbage inside a plant

Flammable garbage in a plant

Chemicals without any covering


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Exhibit 4: The plant floors were disorganized

Instrument not removed after use, blocking hallway.

Old warp beam, chairs and a desk obstructing the plant floor

Dirty and poorly maintained machines

Tools left on the floor after use


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

Yarn without labeling, order or damp protection

Yarn piled up so high and deep that access to back sacks is almost impossible

Different types and colors of yarn lying mixed

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


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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.


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Exhibit 7: The parts stores were also disorganized and dirty

Spares without any labeling or order

No protection to prevent damage and rust

Spares without any labeling or order

Shelves overfilled and disorganized


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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.


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

Broken machine parts being repaired

Parts being cleaned and replaced on jammed loom

Workers investigating a broken loom

Loom parts being disassembled for diagnosis


These firms appear typical of large manufacturers in india china and brazil l.jpg

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

Experimental Firms, mean=2.60

Indian Textiles, mean=2.60

Indian Manufacturing, mean=2.69

Brazil and China Manufacturing, mean=2.67

Management scores


So ran an experiment to evaluate impact of changing the management of large indian firms l.jpg

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 l.jpg

Sample of firms we worked with


Our plants and firms are large by indian us standards l.jpg

Our plants and firms are large by Indian & US standards

Average size of our plants

20

Source: Hsieh and Klenow, 2009


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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 l.jpg

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 l.jpg

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


Adoption of these 38 management practices did rise and particularly in the treatment plants l.jpg

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

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

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

Share of the 38 management practices adopted

Control plants:

Diagnostic July 2009

Non-experiment plants:

No intervention

April 2008

July 2008

October 2008

January 2009

April 2009

July 2009

October 2009

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)


Take away summary points l.jpg

Take away summary points

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

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

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

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

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


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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?


Slide27 l.jpg

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

Large room full of repair workers (the day shift)

Workers spread cloth over lighted plates to spot defects

Defects are repaired by hand or cut out from cloth

Non-fixable defects lead to discounts of up to 75%

27


Previously mending was recorded only to cross check against customers claims for rebates l.jpg

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 l.jpg

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

29


Slide30 l.jpg

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


Defect rates have rapidly fallen in treatment plants l.jpg

Defect rates have rapidly fallen in treatment plants

Diagnostic start

Implementation start

Implementation stop

Control plants

Quality defects index

Treatment plants

Weeks after the start of the intervention (diagnostic phase)

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.


Management impact on quality regressions l.jpg

Management impact on quality, regressions


Slide33 l.jpg

Management practices before and after treatment

Performance of the firms before and after treatment

  • Quality

  • Inventory

  • Operational efficiency

    Why were these practices not introduced before?


Slide34 l.jpg

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

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.


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

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 l.jpg

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


Inventory is falling in treatment firms l.jpg

Inventory is falling in treatment firms

Diagnostic

Implementation

Control firms

Treatment firms

Weeks after the start of the intervention

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 l.jpg

Management impact on inventory, regressions


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

Nuts & bolts sorted as per specifications

Parts like gears, bushes, sorted as per specifications

Tool

storage organized


Slide40 l.jpg

Management practices before and after treatment

Performance of the firms before and after treatment

  • Quality

  • Inventory

  • Operational efficiency

    Why were these practices not introduced before?


Slide41 l.jpg

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


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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 l.jpg

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


Management impact on efficiency regressions l.jpg

Management impact on efficiency, regressions


Estimated impacts on productivity and profitability are large and rising l.jpg

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)


Slide46 l.jpg

46


Slide47 l.jpg

Management practices before and after treatment

Performance of the firms before and after treatment

  • Quality

  • Inventory

  • Operational efficiency

    Why were these practices not introduced before?


So why did these firms have bad management l.jpg

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

48


Why does competition not fix badly managed firms l.jpg

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)

49


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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

50


Back up l.jpg

Back-up


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

Start of Diagnostic

Start of Implementation

Spline + 2 SE

Control plants

Data (+ symbol)

Cubic Spline

Spline - 2 SE

Quality defects index (higher score=lower quality)

Treatment plants

Spline + 2 SE

Cubic Spline

Data (♦ symbol)

Spline - 2 SE

Weeks after the start of the intervention

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.


We work in tarapur because textile mills no longer exist in mumbai l.jpg

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

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