WAREHOUSE PERFORMANCE ASSESSMENT & BENCHMARKING

Transcription

1 The On-Floor Seminar Series Welcomes You to: SESSION 208 WAREHOUSE PERFORMANCE ASSESSMENT & BENCHMARKING John M. Hill Dr. Leon McGinnis

2 PREMISE SINGLE-FACTOR PRODUCTIVITY METRIC COMPARISONS CAN BE RISKY, BUT YOU VE GOT TO START SOMEWHERE!

3 THE PROCESS BEGINS WITH BENCHMARKING Order Fill Rates Order Cycle Times Lines and Orders/Hour Errors Inventory Accuracy Damage Cost/Order Cost as % of Sales Days on Hand

4 MEASURE ORDER FULFILLMENT PERFORMANCE MEASURE CALCULATION TODAY FUTURE VALUE On-Time Delivery Orders On-Time Total Orders Shipped % % $ Orders Filled Complete Order Fill Rate % % $ Total Orders Shipped Order Accuracy Error-Free Orders % % $ Total Orders Shipped Error-Free Lines Line Accuracy % % $ Total Lines Shipped Actual Ship Date Minus Order Cycle Time Hrs Hrs $ Customer Order Date Perfect Order Completion Perfect Deliveries Total Orders Shipped % % $

5 AUDIT INVENTORY MANAGEMENT PERFORMANCE MEASURE CALCULATION TODAY FUTURE VALUE Inventory Accuracy Actual Qty per SKU System Reported Qty % % $ Total Damage $$$ Damaged Inventory % % $ Inventory Value (Cost) Avg. Month Inventory $ Days On Hand Days Days $ Avg. Daily Sales/Month Avg. Occupied Sq. Ft. Storage Utilization Total Storage Capacity % % $ Dock to Stock Time Total Dock to Stock Hrs Hrs Hrs $ Total Receipts Receipt Entry Time - Inventory Visibility Hrs Hrs $ Physical Receipt Time

6 ASSESS WAREHOUSE PRODUCTIVITY MEASURE CALCULATION TODAY FUTURE VALUE Orders per Hour Orders Picked/Packed Total Whse Labor Hrs Ord/Hr Ord/Hr $ Lines per Hour Lines Picked/Packed Total Whse Labor Hrs Lines/Hr Lines/Hr $ Items per Hour Items Picked/Packed Total Whse Labor Hrs Items/Hr Items/Hr $ Cost per Order Total Warehouse Cost Total Orders Shipped $/Order $/Order $ Cost as % of Sales Total Warehouse Cost Total Revenue % % $

7 EVALUATE TRANSPORTATION PERFORMANCE MEASURE CALCULATION TODAY FUTURE VALUE On-Time Deliveries On-Time Deliveries Total Deliveries % % $ Shipment Damage $ Damage % % $ Total Shipment $ Demurrage Costs Total Transport Cost Demurrage % % $ Assessorial Costs Total Transport Cost Assessorials % % $ Missed Appointments Appointments % % $ Total Appointments Billing Error $ Frt. Bill Accuracy % % $ Total Transport Costs

8 MATCH OPPORTUNITIES TO SOLUTIONS ORDER ACCURACY LINE ACCURACY CYCLE TIME DAMAGE DAYS ON HAND STORAGE USAGE DOCK-TO-STOCK VISIBILITY ORDERS / HOUR LINES / HOUR METRICS ON-TIME DELIVERY ORDER FILL RATE COST PER ORDER COST % OF SALES Orders On-Time Total Orders Shipped Orders Filled Complete Total Orders Shipped Error-Free Orders Total Orders Shipped Error-Free Lines Total Lines Shipped Ship Date - (minus) Customer Order Date Total Damage $ Total Inventory $ Avg. Inventory Value Avg. Daily Sales Avg. Inventory Sq. Ft. Storage Capacity Sq. Ft. Average Dock-To-Stock Hours per Receipt Receipt Data Entry - Time of Physical Receipt Orders Picked & Packed Total Labor Hours Lines Picked & Packed Total Labor Hours Total Logistics Costs Total Orders Total Logistics Costs Total Revenue ENABLING TECHNOLOGY & SYSTEMS F cast/plan AOM SCV ADC MH W/LMS TMS

9 QUANTIFY PERFORMANCE IMPROVEMENT POTENTIAL Measure On-Time Delivery Order Accuracy Order Cycle Time Inventory Accuracy Damaged Inventory Days on Hand Storage Utilization Orders per Hour Lines per Hour Cost per Order Cost % of Sales Annual Savings Probable Cost Calculation Total Orders On Time / Total Orders Shipped Errorless Orders / Total Orders Shipped Actual Ship Date (minus) Customer Order Date Actual Quantity by SKU/ Reported Qty. by SKU Total Damage $$$ / Total Inventory Value Avg. Inventory Value ($) / Average Daily Sales $ Avg. Inventory Sq. Ft. / Storage Capacity Sq. Ft. Orders Picked & Packed / Total Whse. Labor Hrs Total Lines Picked / Total Whse. Labor Hrs Total Warehouse Costs / Total Orders Total Warehouse Costs / Total Revenue Current 87% 92% 12 Hrs 96%.75% 50 Days 78% 15/Hr 40/Hr % Target 95% 98% 8 Hrs 99%.50% 42 Days 85% 20/Hr 54/Hr $ % Value $250,000 See above $100,000 See above $100,000 $1 Million $100,000 $864,000 See above See above See above $2.4 Million $1.8 Million

10 KEEPING METRICS IN PERSPECTIVE Logistics measures must be in harmony with a company's overall business strategy. If Amazon.com drove its logistics activities with measures focused solely on reducing delivery costs, it would cripple its ability to serve customers. (Smart managers) are fusing logistics plan(s) with their business strategies, ensuring that what is measured in the field is valued at the top of the organization. Keeping Score: Measuring the Business Value of Logistics in the Supply Chain, CLM, 1999

11 THE GEORGIA TECH CHALLENGE Is it possible to assess the system performance of a warehouse, and compare system performance across warehouses, or across time periods?

12 WHAT TO DO WITH ALL THOSE SINGLE FACTOR PRODUCTIVITY METRICS? Ranking Rating

13 What we need is a handicapping system for warehouse performance

14 DATA ENVELOPMENT ANALYSIS Resources Total Staffing Equipment Replacement Cost Warehouse area Activities Services Lines Shipped Storage Function Accumulation ONE PERFORMANCE INDEX

15 PRODUCTION FUNCTION THEORY For One Input, One Output Production/Output Resource/Input

16 SYSTEM EFFICIENCY CONCEPT Production/Output O A B System efficiency of warehouse B is the ratio OA OB Resource/Input

17 DATA ENVELOPMENT ANALYSIS DEA is a mathematical technique that does this same kind of analysis, but with multiple inputs and multiple outputs.

18 WEB-BASED BASED TOOL Over the Internet Html documents Database At your site Solver Georgia Tech Server

19

20 INVESTMENT COSTS

21 OUTPUT CALCULATOR

22 YOUR RESULTS

23 Results to Date

24 OVER 150 QUALIFIED USERS

25 USER PROFILE Distribution 15% Retail 30% Manufacturing 33% Wholesale 22%

26 OUTPUT SEGMENTATION Broken Case: 49 Full Case: 32 Pallet: 13 Mix: 65 Total: 159

27 BROKEN CASE Input Efficiency Compared Within (49/49) 20 Frequency

28 BROKEN CASE PICKING Pick Rates Lines/Labor hour(broken Case) Lines/Labor hour(broken Case) More More Ave = 17 SD = 27 Frequency

29 FULL CASE Input Efficiency Compared Within (32/32) Frequency

30 More More FULL CASE PICKING FULL CASE PICKING Lines/Labor Hour ( Full Case) Ave = 14 SD = 27.7 Frequency

31 PALLET Input Efficiency Compared within (13/13) 10 Frequency

32 More More PALLET PICKING PALLET PICKING Lines/Labor Hour ( Pallet) Ave = 25 SD = Frequency

33 MIXED Input Efficiency Compared Within (65/65) 20 Frequency

34 MIXED PICKING MIXED PICKING Lines/Labor Hour (Mixed) More More Ave = 10.6 SD = Frequency

35 RESULTS & CONCLUSIONS Bigger is not always better, at least with regard to equipment and labor. There is, however, some evidence that more warehouse space leads to better system efficiency.

36 RESULTS & CONCLUSIONS Labor hours was not found to be a significant factor, by itself, in predicting system efficiency. However, the interaction of labor with investment was found to be significant in the sense that labor hours mitigates the effect of investment (in other words, though high investment warehouses tended to be less efficient than low investment warehouses, the differences becomes less prominent the higher the labor hours).

37 RESULTS & CONCLUSIONS The interaction of investment and space was found to be significant. This means that high investment warehouses are even less efficient if they are also large.

38 RESULTS & CONCLUSIONS No matter how we segment the data, a very large proportion of warehouses are operating at or below 50% system efficiency. While this may reflect industry or business differences, it still represents a very significant opportunity for improvement.

39 RESULTS & CONCLUSIONS The opportunity for improvement seems largest for the segment of warehouses doing predominantly full case picking. In that segment, a smaller proportion of the warehouses are "efficient' than in any other segment, and a larger proportion are operating below 50% efficiency.

40 WHERE DO WE GO FROM HERE?

41 MANY OPPORTUNITIES TO IMPROVE THE BENCHMARKING TOOL Enhance the basic input/output model Enhance the ability to benchmark for technology, practice, & requirements

42 VERSION 2.0 METRICS Inputs Space Capital Labor Inventory # of skus turns Outputs Inbound total replenishment orders received Fulfillment total lines picked, by type total orders shipped

43 VERSION 2.0 Marker Analysis

44 MARKER ANALYSIS DEA Performance Score Performance Marker Attribute

45 MARKER ANALYSIS DEA Performance Score Performance Marker Practice

46 VERSION 2.0 MARKERS Industry Total # SKUs SKU turnover Pick seasonality Pick variability Planning lead time Value adding activities Cube/order Weight/order Space utilization Response time Rush orders Multi-floor? Total # of suppliers WMS? Compliant shipping? Velocity-based slotting? Pick-to-light? RF dispatching? other...

47 On-Line at: Participate in the study, learn about your own system performance, and work with us to improve the practice of warehousing.

48 GETTING STARTED Thanks for coming Questions?