Key messages of lectures 1 to 4

1. 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

2. Improving management in Indian factories Nick Bloom (Stanford Economics) John Van Reenen (Stanford GSB/LSE) Lecture 5 2

3. 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))

4. 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

5. 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?

6. 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

7. 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

8. 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

9. This production technology has not changed much over time:Lowell Mill warping looms (1854, Lowell, Massachusetts) Krill Warp beam

10. 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

11. 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

12. 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

13. 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.

14. 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

15. 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.

16. 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

17. 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

18. 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

19. Sample of firms we worked with

20. Our plants and firms are large by Indian & US standards Average size of our plants 20 Source: Hsieh and Klenow, 2009

21. 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?

22. 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

23. 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

24. 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)

25. 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

26. 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?

27. 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

28. 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

29. Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver 29

30. 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

31. 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.

32. Management impact on quality, regressions

33. 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?

34. 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.

35. 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

36. 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

37. 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.

38. Management impact on inventory, regressions

39. 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

40. 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?

41. 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

42. 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)

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

44. Management impact on efficiency, regressions

45. 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)

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47. 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?

48. 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

49. 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

50. 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