The Perfect Warehouse

The Logistics Institute at Georgia Tech has been developing warehouse and logistics performance measures since 1988. The 2003 TLI/WERC Warehouse Benchmarking Survey was developed and reported by Dr. Ed Frazelle, president and CEO of Logistics Resources International and director of the Logistics Management Series at Georgia Tech. More than 200 individual warehouses from more than 120 corporations are included in the survey.

A wide variety of corporations participated in the 2003 TLI/WERC Warehouse Benchmarking Survey. Warehouses were assigned to and evaluated in three categories – piece shipping warehouses, case shipping warehouses, and pallet shipping warehouses.

A wide variety of warehouse design and management issues were addressed in the survey including:

Warehouse Mission

Building Configuration

Facility Ownership and Management

Facility Activity

Material Handling Systems

Information Handling Systems

Workforce

Metrics

Practices

 

 

Slide 4

 

 

The “average” warehouse in the survey:

Occupies 239,000 square feet

Is 1.7 times longer than deep

Has a maximum clear height of 31.35

Has 26 dock doors

Has a staff of 150 full-time equivalents

Is insourced (75% of the survey warehouses are insourced)

Has a WMS (77% of the survey warehouses have a WMS)

Ships 2.3 million lines per year and 544 thousand orders per year

Houses 57,735 total SKUs and 38,467 active SKUs

Is 82% occupied during normal inventory levels

Is 96% occupied during peak inventory levels

Is non-union (66% of survey warehouses are non-union)

Pays average to above-average wages

 

 

Slide 5

 

 

Dr. Frazelle introduced a new warehouse performance measure as a part of the 2003 TLI/WERC Warehouse Benchmarking Survey. The new measure is the Warehouse Quality Index (WQI). The indicator is the product of the inventory accuracy and the shipping accuracy in the warehouse. WQI is an aggregate measure of warehouse quality performance.

The average WQI for the survey warehouses is 93.2%. The median WQI for the survey warehouse is 97.1%. The average WQI for the top 25% quality performers is 99.65%. The median WQI for the top 25% of quality performers is 99.76%.

All survey warehouses were ranked in quality performance based on their warehouse quality index.

 

 

Slide 6

 

 

All warehouses were also ranked on the basis of their productivity performance. The productivity of piece shipping warehouses was measured based on the lines shipped per total person-hour; the productivity of the case shipping warehouses was measured based on the cases shipped per total person-hour; and the productivity of the pallet shipping warehouses was measured based on the pallets shipped per total person-hour.

 

 

Slide 7

 

 

Survey warehouses were assigned a warehouse performance rank % combining productivity and quality performance. The warehouse performance rank % is the average of the warehouse’s productivity and quality rank %.

 

 

Slide 8

 

 

The large majority of warehouses in the survey house and ship finished goods inventory.

 

 

Slide 9

 

 

Warehouses were assigned to and evaluated in three categories – piece shipping warehouses (41.4%), case shipping warehouses (41.4%), and pallet shipping warehouses (17.7%).

 

 

 

 

Slide 10

 

 

Warehouses in the 2003 TLI/WERC Warehouse Benchmarking Survey ship to a variety of locations:

A retailers warehouse (28.1%)

A wholesalers warehouse (14.6%)

A distributor (12.9%)

A dealership (9.4%

A store (8.2%)

A house (7.0%)

A stockroom (4.7%)

A technician (1.2%)

 

 

Slide 11

 

 

The average size of a warehouse in the survey is 239,985 square feet. The maximum size is 1,200,000 square feet.
 

Of the ten warehouses with the best warehouse performance rank %, only one was larger than 300,000 square feet.
 

Dr. Frazelle notes that “It is more difficult to achieve high productivity and quality performance in large warehouses because travel distances are naturally extended in large warehouses, supervision is more difficult in large warehouses, and communication is more difficult. The key to making large warehouses efficient is to sub-divide a large warehouse into small warehouses within the warehouse. Effective criteria for sub-dividing large warehouses include activity zones, product commodity zones, and/or order completion zones.”

 

 

 

 

 

Slide 12

 

 

The average length to width ratio of the warehouses in the survey is 2.1. The median length to width ratio of the warehouses in the survey was 1.7. In general, those warehouses with length to width ratios in the range of 1.5 to 2.0 had better warehouse productivity performance.

 

 

Slide 13

 

 

The average clear height of warehouses in the 2003 TLI/WERC Warehouse Benchmarking Survey is 31.7 feet. The median clear height of warehouses in the 2003 TLI/WERC Warehouse Benchmarking Survey is 31.0 feet.

According to Dr. Frazelle, “The key to excellent building cube management is to locate activities that require high-bay space in high-bay areas and to locate activities without high-bay requirements in low-bay areas or on mezzanines.”

 

 

Slide 14

 

 

75% of the warehouses in the survey are in-sourced and 25% are outsourced. In-sourced warehouses are expecting to continue to in-sourcing in the future and outsourced warehouses are expecting to continue outsourcing.

 

 

Slide 15

 

 

Those warehouses that are outsourced in the 2003 TLI/WERC Warehouse Benchmarking Survey are outsourced to a wide variety of third-party logistics providers. A list of outsource providers used by organizations in the survey follows:

Almacenar Operador Logistico, Anthar Logistics, APL Logistics, Atlanta Bonded Warehouse, BLJC, Camion Transport AG, DANZAS, DIAKINISIS SA, Exel, F&M Logistics, Genco Distribution Systems, General Warehouse, Green Field Agencies, Inprax, J.D. Smith and Sons Ltd., Kenco Logistics, Linfox Pty Ltd, Menlo Logistics, Peoples Services, Power Logistics, REWICO, Shenzhen ST-Anda Logistics Co. LTD, Sunland, Tibbett and Britten, TNT Logistics Malaysia, USCO Logistics, Zust Ambrosetti Group

21% of the survey companies with outsourced warehouses report that the third-party’s services GREATLY EXCEEDS expectations

37% of the survey companies with outsourced warehouses report that the third-party’s services EXCEEDS expectations

35% of the survey companies with outsourced warehouses report that the third-party’s services MEETS expectations

2% of the survey companies with outsourced warehouses report that the third-party’s services FALL BELOW expectations

21% of the survey companies with outsourced warehouses report that the third-party’s services FALL FAR BELOW expectations

 

 

 

 

 

 

Slide 16

 

 

 Survey participants were asked to report that % occupancy of their warehouse at normal and peak inventory levels.

 The “average” warehouse in the survey is 82% full at “normal” inventory levels and 96% full at “peak” inventory levels.

 According to Dr. Frazelle, “It is critical to maintain occupancy levels between 80% and 90%. Warehouse productivity and safety decline sharply once warehouse occupancy exceeds 85%. That threshold has been revealed in study after study. There is almost something supernatural about the 85% threshold. One day it dawned on me that 85% is the ratio of the number of working days in a week to the total number of days in a week – 6/7 = 85%. I think God is telling us that just like we need a Sabbath rest to avoid problems in our lives, some of the warehouse locations need a rest to help avoid productivity and safety problems in the warehouse.”

 

 

Slide 17

 

 

Survey participants were asked to report their productivity loss associated with occupancy levels exceeding 85%.

The average expected productivity loss due to excessive occupancy is 9%. “In my consulting experience most organizations don’t really know the productivity loss they experience during excess occupancy. The loss in productivity and risk of accidents associated with excess occupancy is much higher than most organizations estimate. I encourage our clients to measure their productivity, quality, and safety performance associated with various occupancy levels. That will reveal the true performance degradation associated with excess occupancy.”

 

 

Slide 18

 

 

It Pays to Measure

Survey participants were asked to report the number and type of warehouse performance indicators in place in their warehouse. The number of metrics was correlated with the warehouse performance rank % for all warehouses. Those warehouses with the most metrics (11 to 15) had 25% better performance than those with an average number of metrics (6 to 10) and a 33% better performance than those warehouses with few metrics (1 to 5).

According to Dr. Frazelle, “People behave based on the way they are measured. If they are not measured at all, they may not perform at all.”

 

 

Slide 19

 

 

Productivity Performance Measures

Survey participants were asked to report the types of overall warehouse productivity performance indicators in place.

54.6% track total cases shipped per man-hour

46.9% track total lines shipped per man-hour

36.9% track total pallets shipped per man-hour

34.6% track total pieces shipped per man-hour

11.5% track total pounds shipped per man-hour

 

 

Slide 20

 

 

Piece Shipping Productivity

The warehouse productivity performance for piece shipping warehouses was calculated for each warehouse as the total lines shipped per year divided by the total person-hours (including operators, supervisors, and managers) per year. The average lines per person-hour for piece shipping warehouses is 10.94. The median lines per person-hour for piece shipping warehouses is 8.24. The distribution and range of productivity performance for piece shipping warehouses is provided in the figure.

 

 

Slide 21

 

 

Case Shipping Productivity

The warehouse productivity performance for case shipping warehouses was calculated for each warehouse as the total cases shipped per year divided by the total person-hours (including operators, supervisors, and managers) per year. The average cases per person-hour for case shipping warehouses is 100 cases per person-hour. The median cases per person-hour for case shipping warehouses is 64. The distribution and range of productivity performance for case shipping warehouses is provided in the figure.

 

 

 

Slide 22

 

 

Quality Metrics in Survey Warehouses

Survey participants were asked to report the types of quality metrics tracked in their warehouses. The most popular warehouse quality indicator is location inventory accuracy. 78.8% of the survey warehouses track location inventory accuracy. The next most popular warehouse quality indicator is line item picking accuracy. 67.3% of survey warehouses track line item picking accuracy.

 

 

Slide 23

 

 

Order Line Shipping Accuracy

Order line shipping accuracy, the % of order lines shipped without errors was the most popular warehouse quality indicator among survey warehouses. The average order line shipping accuracy was 99.982%. The median order line shipping accuracy was 99.5%.

 

 

Slide 24

 

 

Location Inventory Accuracy

Location inventory accuracy, the % of warehouse locations without discrepancies, was the second most popular warehouse quality indicator among survey warehouses. The average location inventory accuracy was 94.19%. The median location inventory accuracy was 98.6$.

 

 

Slide 25

 

 

Warehouse Quality Index

The warehouse quality index (WQI) is a new metric proposed by Dr. Frazelle. According to Dr. Frazelle, “The warehouse quality index is the product of the location inventory accuracy and the order line shipping accuracy. It captures the inbound and outbound quality performance of a warehouses. It is also fairly easy to capture and compare since most warehouses track inventory accuracy and shipping accuracy. The average WQI among survey warehouses was 93.2%. The median WQI among survey warehouses was 97.1%. The average WQI for the top quartile was 99.65%. The median WQI for the top quartile was 99.76%.”

 

 

Slide 26

 

 

Workforce Turnover

According to Dr. Frazelle, “Workforce turnover is a key indicator of workforce morale, working conditions, and management capability. The average workforce turnover among survey warehouse was 15%; the median was 9%. Workforce turnover in warehouses in general is much higher that that experienced by survey warehouses. That suggests that the warehouses in the survey have high workforce morale, working conditions, and management capability. That also suggests that the performance statistics reported by survey warehouses is much better than for the general population of warehouses.”

 

 

Slide 27

 

 

Workforce Turnover vs. Warehouse Quality

“I correlated workforce turnover with a variety of warehouse performance statistics. The strongest and most significant correlation was between workforce turnover and the warehouse quality index. Warehouses with workforce turnover rates less than 5% had an average warehouse quality index of 96.08%; those with turnover rates ranging between 5% and 10% had an average warehouse quality index of 95.4%; those with turnover rates between 10% and 25% had an average warehouse quality index of 93.57%; and those with a turnover rate higher than 25% had an average warehouse quality index of 91.57%. If companies would truly measure the cost of poor quality in their warehouses they would quickly come to the conclusion that investments in workforce morale yield a high return-on-investment.”

 

 

Slide 28

 

 

It pays to pay!

“The large majority of survey warehouses pay average to above averages wages. Those paying above average wages had an average warehouse quality index of 98.3% compared to a warehouse quality index of 90.3% for those warehouse offering average wages. Again, if companies would consider the true cost of poor quality they would quickly come to the conclusion that workforce investments yield a high dividend.”

 

 

Slide 29

 

 

Operator to Supervisor Ratio

According to Dr. Frazelle, “A key indicator of management philosophy is the ratio of operators to supervisors. Some warehouses try to cut corners by cutting back on supervision and management. Those results can be devastating. The average operator to supervisor ratio among survey warehouses was 11.3; the median 10.2. The ratio yielding the best productivity performance was 12. The ratio yielding the best quality performance was 8.”

 

 

Slide 30

 

 

 

 

Slide 31

 

 

 

 

Slide 32

 

 

% Active SKUs

Survey participants were asked to report the % of SKUs with activity in a 12 month period. The average % active SKUs was 78.2%; the median 85.1%. According to Dr. Frazelle, “Inactive SKUs are inevitable in warehouse operations. The key to success is managing the inactive SKUs. If possible, the inactive SKUs should be eliminated, consolidated into a central facility specializing in handling slow-moving SKUs, and removed from the pick lines housing fast and medium-moving SKUs. It is also helpful to batch pick slow moving items to reduce the travel time between picks.”

 

 

Slide 33

 

 

SKU Activity

According to Dr. Frazelle, “A key predictor of warehouse productivity is the popularity of the SKUs in the warehouse. I look at a simple ratio of the annual order lines shipped from the warehouse to the total number of SKUs in the warehouse. The ratio, lines shipped per SKU, indicates the number of request per year for the “average” SKU in the warehouse. There is a strong correlation between the activity of the SKUs in a warehouse and the productivity performance of the warehouse.”

The average lines shipped per SKU for all survey warehouses was 959; the median, 269.

 

 

Slide 34

 

 

Lines Shipped per SKU for Piece Shipping Warehouses

The average lines shipped per SKU for piece shipping warehouses was 609; the median, 71.

 

 

 

Slide 35

 

 

According to Dr. Frazelle, “A key predictor of warehouse productivity is the popularity of the SKUs in the warehouse. I look at a simple ratio of the annual order lines shipped from the warehouse to the total number of SKUs in the warehouse. The ratio, lines shipped per SKU, indicates the number of request per year for the “average” SKU in the warehouse. There is a strong correlation between the activity of the SKUs in a warehouse and the productivity performance of the warehouse. You can see in the figure how the productivity rank % increases as the lines per SKU ratio declines.”

 

 

 

Slide 36

 

 

According to Dr. Frazelle, “A key predictor of warehouse productivity is the popularity of the SKUs in the warehouse. I look at a simple ratio of the annual order lines shipped from the warehouse to the total number of SKUs in the warehouse. The ratio, lines shipped per SKU, indicates the number of request per year for the “average” SKU in the warehouse. There is a strong correlation between the activity of the SKUs in a warehouse and the productivity performance of the warehouse.”

 

 

 

Slide 37

 

 

WMS Satisfaction

Survey participants were asked to indicate whether or not they had a WMS, who the provider was, and the level of satisfaction with the system. A wide variety of WMS providers are represented in the survey. 77% of survey participants have a WMS. That again suggests that the warehouse operators in the survey are among the most advanced. Of the survey participants with a WMS;

16% were not satisfied with their WMS

58.5% were satisfied with their WMS

25.5% were highly satisfied with their WMS

 

 

Slide 38

 

 

Warehouse Communication Methods

Survey participants were asked to indicated the means of communication used in their warehouses.

84% use paper

57% use bar code scanning

51% use handheld RF terminals

37% used vehicle mounted RF terminals

10% use pick-to-light systems

7% use optical character recognition

6% use handsfree RF terminals

5% use RF tags

2% use voice headsets

2% use PDAs

 

 

 

Slide 39

 

 

It pays to communicate!

According to Dr. Frazelle, “I correlated the warehouse performance rank %, an overall measure of productivity and quality performance, with many different factors. One of the strongest correlating factors was the level of information handling automation deployed in the warehouse. There seemed to be a threshold required to achieve an excellent performance rating. The key is to use a WMS and to employ the correct paperless technology in each activity in the warehouse. Those warehouses with a WMS and the right mix of paperless communication technologies had significantly better warehouse productivity and quality performance than those without a high level of information handling automation.”

 

 

Slide 40

 

 

Pallet Storage Modes

Survey participants were asked to indicate the types of storage devices used for pallet storage.

79.7% use floor storage.

86.0 % use single-deep pallet racking.

35.7% use double-deep pallet racking.

19.6% use push-back pallet racking.

24.5% use drive in/through pallet racking.

30.1% use pallet flow rack.

 

 

Slide 41

 

 

Case Picking Devices

Survey participants were asked to indicate the types of devices they use in case picking.

72% use pallet jacks for case picking.

62.9% use counterbalance lift trucks for case picking.

40.2% use man-aboard order picker trucks for case picking.

18.2% use pick-to-belt conveyors for case picking.

15.9% use sortation conveyors for case picking.

9.1% use layer pickers for case picking.

8.3% use tuggers for case picking.

 

 

Slide 42

 

 

Broken Case Picking Equipment

Survey participants were asked to indicate the types of equipment they use in broken case picking.

78.7% use bin shelving for broken case picking.

50.0% use carton flow rack for broken case picking.

20.2% use storage drawers for broken case picking.

11.7% use vertical carousels for broken case picking.

10.6% use horizontal carousels for broken case picking.

4.3% use automated dispensing for broken case picking.

2.1% use a miniload ASRS for broken case picking.

 

 

Slide 43

 

 

Material Handling Automation

According to Dr. Frazelle, “I correlated the level of material handling automation with the productivity performance of survey warehouses. The warehouses with the worst productivity performance had MUCH ABOVE AVERAGE levels of material handling automation. Those warehouses with ABOVE AVEREAGE, BELOW AVERAGE, and MUCH BELOW AVERAGE levels of material handling automation also had low productivity performance. The warehouses with AVERAGE levels of material handling had the best productivity performance. The conclusion I draw is that some material handling equipment is needed to support the warehouse workforce, move efficiently between warehouse locations, access upper levels within the warehouse, and handle heavy loads; however, a high level of automation may create excess complexity, hamper operating flexibility, and yield a low return on investment. It’s not to say that material handling automation is inherently a bad decision, it’s more to say that the decision to invest heavily in high levels of material handling automation should be made very carefully.”

 

 

Slide 44

 

 

Warehouse Practices

Survey participants were asked to indicate some of a variety of practices that are in place in their warehouses.

81.2% use cross-training.

69.6% use productivity or time standards.

65.9% have product cube information.

60.6% use advance shipment notifications.

56.1% have continuous improvement teams.

53.3% use popularity slotting or golden zoning.

49.7% use cross-docking.

48.2% have an ergonomics program.

44.0% use directed putaway.

34.8% use activity-based costing.

16.1% use interleaving.

 

 

Slide 45

 

 

It pays to practice!

According to Dr. Frazelle, “Not surprisingly, there was a direct and strong correlation between the number of world-class practices in place and the overall productivity and quality performance of the warehouse. There is an old adage in sports that says, “You play the way you practice.” That’s the reason that coaches put so much emphasis on practice! It’s the same in warehousing, you perform the way your practice!”

 

 

Slide 46

 

 

 

 

Slide 47

 

 

 

 

Slide 48

 

 

 

 

Slide 1

 

 

The Logistics Institute at Georgia Tech has been developing warehouse and logistics performance measures since 1988. The 2003 TLI/WERC Warehouse Benchmarking Survey was developed and reported by Dr. Ed Frazelle, president and CEO of Logistics Resources International and director of the Logistics Management Series at Georgia Tech. More than 200 individual warehouses from more than 120 corporations are included in the survey.

 

 

 

Slide 2

 

 

A wide variety of corporations participated in the 2003 TLI/WERC Warehouse Benchmarking Survey. Warehouses were assigned to and evaluated in three categories – piece shipping warehouses, case shipping warehouses, and pallet shipping warehouses.

 

 

Slide 3

 

 

A wide variety of warehouse design and management issues were addressed in the survey including:

Warehouse Mission

Building Configuration

Facility Ownership and Management

Facility Activity

Material Handling Systems

Information Handling Systems

Workforce

Metrics

Practices

 

 

Slide 4

 

 

The “average” warehouse in the survey:

Occupies 239,000 square feet

Is 1.7 times longer than deep

Has a maximum clear height of 31.35

Has 26 dock doors

Has a staff of 150 full-time equivalents

Is insourced (75% of the survey warehouses are insourced)

Has a WMS (77% of the survey warehouses have a WMS)

Ships 2.3 million lines per year and 544 thousand orders per year

Houses 57,735 total SKUs and 38,467 active SKUs

Is 82% occupied during normal inventory levels

Is 96% occupied during peak inventory levels

Is non-union (66% of survey warehouses are non-union)

Pays average to above-average wages

 

 

Slide 5

 

 

Dr. Frazelle introduced a new warehouse performance measure as a part of the 2003 TLI/WERC Warehouse Benchmarking Survey. The new measure is the Warehouse Quality Index (WQI). The indicator is the product of the inventory accuracy and the shipping accuracy in the warehouse. WQI is an aggregate measure of warehouse quality performance.

The average WQI for the survey warehouses is 93.2%. The median WQI for the survey warehouse is 97.1%. The average WQI for the top 25% quality performers is 99.65%. The median WQI for the top 25% of quality performers is 99.76%.

All survey warehouses were ranked in quality performance based on their warehouse quality index.

 

 

Slide 6

 

 

All warehouses were also ranked on the basis of their productivity performance. The productivity of piece shipping warehouses was measured based on the lines shipped per total person-hour; the productivity of the case shipping warehouses was measured based on the cases shipped per total person-hour; and the productivity of the pallet shipping warehouses was measured based on the pallets shipped per total person-hour.

 

 

Slide 7

 

 

Survey warehouses were assigned a warehouse performance rank % combining productivity and quality performance. The warehouse performance rank % is the average of the warehouse’s productivity and quality rank %.

 

 

Slide 8

 

 

The large majority of warehouses in the survey house and ship finished goods inventory.

 

 

Slide 9

 

 

Warehouses were assigned to and evaluated in three categories – piece shipping warehouses (41.4%), case shipping warehouses (41.4%), and pallet shipping warehouses (17.7%).

 

 

 

 

Slide 10

 

 

Warehouses in the 2003 TLI/WERC Warehouse Benchmarking Survey ship to a variety of locations:

A retailers warehouse (28.1%)

A wholesalers warehouse (14.6%)

A distributor (12.9%)

A dealership (9.4%

A store (8.2%)

A house (7.0%)

A stockroom (4.7%)

A technician (1.2%)

 

 

Slide 11

 

 

The average size of a warehouse in the survey is 239,985 square feet. The maximum size is 1,200,000 square feet.
 

Of the ten warehouses with the best warehouse performance rank %, only one was larger than 300,000 square feet.
 

Dr. Frazelle notes that “It is more difficult to achieve high productivity and quality performance in large warehouses because travel distances are naturally extended in large warehouses, supervision is more difficult in large warehouses, and communication is more difficult. The key to making large warehouses efficient is to sub-divide a large warehouse into small warehouses within the warehouse. Effective criteria for sub-dividing large warehouses include activity zones, product commodity zones, and/or order completion zones.”

 

 

 

 

 

Slide 12

 

 

The average length to width ratio of the warehouses in the survey is 2.1. The median length to width ratio of the warehouses in the survey was 1.7. In general, those warehouses with length to width ratios in the range of 1.5 to 2.0 had better warehouse productivity performance.

 

 

Slide 13

 

 

The average clear height of warehouses in the 2003 TLI/WERC Warehouse Benchmarking Survey is 31.7 feet. The median clear height of warehouses in the 2003 TLI/WERC Warehouse Benchmarking Survey is 31.0 feet.

According to Dr. Frazelle, “The key to excellent building cube management is to locate activities that require high-bay space in high-bay areas and to locate activities without high-bay requirements in low-bay areas or on mezzanines.”

 

 

Slide 14

 

 

75% of the warehouses in the survey are in-sourced and 25% are outsourced. In-sourced warehouses are expecting to continue to in-sourcing in the future and outsourced warehouses are expecting to continue outsourcing.

 

 

Slide 15

 

 

Those warehouses that are outsourced in the 2003 TLI/WERC Warehouse Benchmarking Survey are outsourced to a wide variety of third-party logistics providers. A list of outsource providers used by organizations in the survey follows:

Almacenar Operador Logistico, Anthar Logistics, APL Logistics, Atlanta Bonded Warehouse, BLJC, Camion Transport AG, DANZAS, DIAKINISIS SA, Exel, F&M Logistics, Genco Distribution Systems, General Warehouse, Green Field Agencies, Inprax, J.D. Smith and Sons Ltd., Kenco Logistics, Linfox Pty Ltd, Menlo Logistics, Peoples Services, Power Logistics, REWICO, Shenzhen ST-Anda Logistics Co. LTD, Sunland, Tibbett and Britten, TNT Logistics Malaysia, USCO Logistics, Zust Ambrosetti Group

21% of the survey companies with outsourced warehouses report that the third-party’s services GREATLY EXCEEDS expectations

37% of the survey companies with outsourced warehouses report that the third-party’s services EXCEEDS expectations

35% of the survey companies with outsourced warehouses report that the third-party’s services MEETS expectations

2% of the survey companies with outsourced warehouses report that the third-party’s services FALL BELOW expectations

21% of the survey companies with outsourced warehouses report that the third-party’s services FALL FAR BELOW expectations

 

 

 

 

 

 

Slide 16

 

 

 Survey participants were asked to report that % occupancy of their warehouse at normal and peak inventory levels.

 The “average” warehouse in the survey is 82% full at “normal” inventory levels and 96% full at “peak” inventory levels.

 According to Dr. Frazelle, “It is critical to maintain occupancy levels between 80% and 90%. Warehouse productivity and safety decline sharply once warehouse occupancy exceeds 85%. That threshold has been revealed in study after study. There is almost something supernatural about the 85% threshold. One day it dawned on me that 85% is the ratio of the number of working days in a week to the total number of days in a week – 6/7 = 85%. I think God is telling us that just like we need a Sabbath rest to avoid problems in our lives, some of the warehouse locations need a rest to help avoid productivity and safety problems in the warehouse.”

 

 

Slide 17

 

 

Survey participants were asked to report their productivity loss associated with occupancy levels exceeding 85%.

The average expected productivity loss due to excessive occupancy is 9%. “In my consulting experience most organizations don’t really know the productivity loss they experience during excess occupancy. The loss in productivity and risk of accidents associated with excess occupancy is much higher than most organizations estimate. I encourage our clients to measure their productivity, quality, and safety performance associated with various occupancy levels. That will reveal the true performance degradation associated with excess occupancy.”

 

 

Slide 18

 

 

It Pays to Measure

Survey participants were asked to report the number and type of warehouse performance indicators in place in their warehouse. The number of metrics was correlated with the warehouse performance rank % for all warehouses. Those warehouses with the most metrics (11 to 15) had 25% better performance than those with an average number of metrics (6 to 10) and a 33% better performance than those warehouses with few metrics (1 to 5).

According to Dr. Frazelle, “People behave based on the way they are measured. If they are not measured at all, they may not perform at all.”

 

 

Slide 19

 

 

Productivity Performance Measures

Survey participants were asked to report the types of overall warehouse productivity performance indicators in place.

54.6% track total cases shipped per man-hour

46.9% track total lines shipped per man-hour

36.9% track total pallets shipped per man-hour

34.6% track total pieces shipped per man-hour

11.5% track total pounds shipped per man-hour

 

 

Slide 20

 

 

Piece Shipping Productivity

The warehouse productivity performance for piece shipping warehouses was calculated for each warehouse as the total lines shipped per year divided by the total person-hours (including operators, supervisors, and managers) per year. The average lines per person-hour for piece shipping warehouses is 10.94. The median lines per person-hour for piece shipping warehouses is 8.24. The distribution and range of productivity performance for piece shipping warehouses is provided in the figure.

 

 

Slide 21

 

 

Case Shipping Productivity

The warehouse productivity performance for case shipping warehouses was calculated for each warehouse as the total cases shipped per year divided by the total person-hours (including operators, supervisors, and managers) per year. The average cases per person-hour for case shipping warehouses is 100 cases per person-hour. The median cases per person-hour for case shipping warehouses is 64. The distribution and range of productivity performance for case shipping warehouses is provided in the figure.

 

 

 

Slide 22

 

 

Quality Metrics in Survey Warehouses

Survey participants were asked to report the types of quality metrics tracked in their warehouses. The most popular warehouse quality indicator is location inventory accuracy. 78.8% of the survey warehouses track location inventory accuracy. The next most popular warehouse quality indicator is line item picking accuracy. 67.3% of survey warehouses track line item picking accuracy.

 

 

Slide 23

 

 

Order Line Shipping Accuracy

Order line shipping accuracy, the % of order lines shipped without errors was the most popular warehouse quality indicator among survey warehouses. The average order line shipping accuracy was 99.982%. The median order line shipping accuracy was 99.5%.

 

 

Slide 24

 

 

Location Inventory Accuracy

Location inventory accuracy, the % of warehouse locations without discrepancies, was the second most popular warehouse quality indicator among survey warehouses. The average location inventory accuracy was 94.19%. The median location inventory accuracy was 98.6$.

 

 

Slide 25

 

 

Warehouse Quality Index

The warehouse quality index (WQI) is a new metric proposed by Dr. Frazelle. According to Dr. Frazelle, “The warehouse quality index is the product of the location inventory accuracy and the order line shipping accuracy. It captures the inbound and outbound quality performance of a warehouses. It is also fairly easy to capture and compare since most warehouses track inventory accuracy and shipping accuracy. The average WQI among survey warehouses was 93.2%. The median WQI among survey warehouses was 97.1%. The average WQI for the top quartile was 99.65%. The median WQI for the top quartile was 99.76%.”

 

 

Slide 26

 

 

Workforce Turnover

According to Dr. Frazelle, “Workforce turnover is a key indicator of workforce morale, working conditions, and management capability. The average workforce turnover among survey warehouse was 15%; the median was 9%. Workforce turnover in warehouses in general is much higher that that experienced by survey warehouses. That suggests that the warehouses in the survey have high workforce morale, working conditions, and management capability. That also suggests that the performance statistics reported by survey warehouses is much better than for the general population of warehouses.”

 

 

Slide 27

 

 

Workforce Turnover vs. Warehouse Quality

“I correlated workforce turnover with a variety of warehouse performance statistics. The strongest and most significant correlation was between workforce turnover and the warehouse quality index. Warehouses with workforce turnover rates less than 5% had an average warehouse quality index of 96.08%; those with turnover rates ranging between 5% and 10% had an average warehouse quality index of 95.4%; those with turnover rates between 10% and 25% had an average warehouse quality index of 93.57%; and those with a turnover rate higher than 25% had an average warehouse quality index of 91.57%. If companies would truly measure the cost of poor quality in their warehouses they would quickly come to the conclusion that investments in workforce morale yield a high return-on-investment.”

 

 

Slide 28

 

 

It pays to pay!

“The large majority of survey warehouses pay average to above averages wages. Those paying above average wages had an average warehouse quality index of 98.3% compared to a warehouse quality index of 90.3% for those warehouse offering average wages. Again, if companies would consider the true cost of poor quality they would quickly come to the conclusion that workforce investments yield a high dividend.”

 

 

Slide 29

 

 

Operator to Supervisor Ratio

According to Dr. Frazelle, “A key indicator of management philosophy is the ratio of operators to supervisors. Some warehouses try to cut corners by cutting back on supervision and management. Those results can be devastating. The average operator to supervisor ratio among survey warehouses was 11.3; the median 10.2. The ratio yielding the best productivity performance was 12. The ratio yielding the best quality performance was 8.”

 

 

Slide 30

 

 

 

 

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Slide 32

 

 

% Active SKUs

Survey participants were asked to report the % of SKUs with activity in a 12 month period. The average % active SKUs was 78.2%; the median 85.1%. According to Dr. Frazelle, “Inactive SKUs are inevitable in warehouse operations. The key to success is managing the inactive SKUs. If possible, the inactive SKUs should be eliminated, consolidated into a central facility specializing in handling slow-moving SKUs, and removed from the pick lines housing fast and medium-moving SKUs. It is also helpful to batch pick slow moving items to reduce the travel time between picks.”

 

 

Slide 33

 

 

SKU Activity

According to Dr. Frazelle, “A key predictor of warehouse productivity is the popularity of the SKUs in the warehouse. I look at a simple ratio of the annual order lines shipped from the warehouse to the total number of SKUs in the warehouse. The ratio, lines shipped per SKU, indicates the number of request per year for the “average” SKU in the warehouse. There is a strong correlation between the activity of the SKUs in a warehouse and the productivity performance of the warehouse.”

The average lines shipped per SKU for all survey warehouses was 959; the median, 269.

 

 

Slide 34

 

 

Lines Shipped per SKU for Piece Shipping Warehouses

The average lines shipped per SKU for piece shipping warehouses was 609; the median, 71.

 

 

 

Slide 35

 

 

According to Dr. Frazelle, “A key predictor of warehouse productivity is the popularity of the SKUs in the warehouse. I look at a simple ratio of the annual order lines shipped from the warehouse to the total number of SKUs in the warehouse. The ratio, lines shipped per SKU, indicates the number of request per year for the “average” SKU in the warehouse. There is a strong correlation between the activity of the SKUs in a warehouse and the productivity performance of the warehouse. You can see in the figure how the productivity rank % increases as the lines per SKU ratio declines.”

 

 

 

Slide 36

 

 

According to Dr. Frazelle, “A key predictor of warehouse productivity is the popularity of the SKUs in the warehouse. I look at a simple ratio of the annual order lines shipped from the warehouse to the total number of SKUs in the warehouse. The ratio, lines shipped per SKU, indicates the number of request per year for the “average” SKU in the warehouse. There is a strong correlation between the activity of the SKUs in a warehouse and the productivity performance of the warehouse.”

 

 

 

Slide 37

 

 

WMS Satisfaction

Survey participants were asked to indicate whether or not they had a WMS, who the provider was, and the level of satisfaction with the system. A wide variety of WMS providers are represented in the survey. 77% of survey participants have a WMS. That again suggests that the warehouse operators in the survey are among the most advanced. Of the survey participants with a WMS;

16% were not satisfied with their WMS

58.5% were satisfied with their WMS

25.5% were highly satisfied with their WMS

 

 

Slide 38

 

 

Warehouse Communication Methods

Survey participants were asked to indicated the means of communication used in their warehouses.

84% use paper

57% use bar code scanning

51% use handheld RF terminals

37% used vehicle mounted RF terminals

10% use pick-to-light systems

7% use optical character recognition

6% use handsfree RF terminals

5% use RF tags

2% use voice headsets

2% use PDAs

 

 

 

Slide 39

 

 

It pays to communicate!

According to Dr. Frazelle, “I correlated the warehouse performance rank %, an overall measure of productivity and quality performance, with many different factors. One of the strongest correlating factors was the level of information handling automation deployed in the warehouse. There seemed to be a threshold required to achieve an excellent performance rating. The key is to use a WMS and to employ the correct paperless technology in each activity in the warehouse. Those warehouses with a WMS and the right mix of paperless communication technologies had significantly better warehouse productivity and quality performance than those without a high level of information handling automation.”

 

 

Slide 40

 

 

Pallet Storage Modes

Survey participants were asked to indicate the types of storage devices used for pallet storage.

79.7% use floor storage.

86.0 % use single-deep pallet racking.

35.7% use double-deep pallet racking.

19.6% use push-back pallet racking.

24.5% use drive in/through pallet racking.

30.1% use pallet flow rack.

 

 

Slide 41

 

 

Case Picking Devices

Survey participants were asked to indicate the types of devices they use in case picking.

72% use pallet jacks for case picking.

62.9% use counterbalance lift trucks for case picking.

40.2% use man-aboard order picker trucks for case picking.

18.2% use pick-to-belt conveyors for case picking.

15.9% use sortation conveyors for case picking.

9.1% use layer pickers for case picking.

8.3% use tuggers for case picking.

 

 

Slide 42

 

 

Broken Case Picking Equipment

Survey participants were asked to indicate the types of equipment they use in broken case picking.

78.7% use bin shelving for broken case picking.

50.0% use carton flow rack for broken case picking.

20.2% use storage drawers for broken case picking.

11.7% use vertical carousels for broken case picking.

10.6% use horizontal carousels for broken case picking.

4.3% use automated dispensing for broken case picking.

2.1% use a miniload ASRS for broken case picking.

 

 

Slide 43

 

 

Material Handling Automation

According to Dr. Frazelle, “I correlated the level of material handling automation with the productivity performance of survey warehouses. The warehouses with the worst productivity performance had MUCH ABOVE AVERAGE levels of material handling automation. Those warehouses with ABOVE AVEREAGE, BELOW AVERAGE, and MUCH BELOW AVERAGE levels of material handling automation also had low productivity performance. The warehouses with AVERAGE levels of material handling had the best productivity performance. The conclusion I draw is that some material handling equipment is needed to support the warehouse workforce, move efficiently between warehouse locations, access upper levels within the warehouse, and handle heavy loads; however, a high level of automation may create excess complexity, hamper operating flexibility, and yield a low return on investment. It’s not to say that material handling automation is inherently a bad decision, it’s more to say that the decision to invest heavily in high levels of material handling automation should be made very carefully.”

 

 

Slide 44

 

 

Warehouse Practices

Survey participants were asked to indicate some of a variety of practices that are in place in their warehouses.

81.2% use cross-training.

69.6% use productivity or time standards.

65.9% have product cube information.

60.6% use advance shipment notifications.

56.1% have continuous improvement teams.

53.3% use popularity slotting or golden zoning.

49.7% use cross-docking.

48.2% have an ergonomics program.

44.0% use directed putaway.

34.8% use activity-based costing.

16.1% use interleaving.

 

 

Slide 45

 

 

It pays to practice!

According to Dr. Frazelle, “Not surprisingly, there was a direct and strong correlation between the number of world-class practices in place and the overall productivity and quality performance of the warehouse. There is an old adage in sports that says, “You play the way you practice.” That’s the reason that coaches put so much emphasis on practice! It’s the same in warehousing, you perform the way your practice!”

 

 

Slide 46

 

 

 

 

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Slide 48