倉儲與配送管理美國的一個倉庫管理系統(tǒng)(PPT 63頁).ppt

1、Internet-based Data Envelopment Analysis for Warehousing,Outline,The problem The current solution The need A new solution How it works Internet deployment and results to date Future directions,Performance Assessment,How well are you performing? Do you have opportunities to improve?,Warehouse Operati

2、ons,Receiving Function (inbound),Unload,Inspect,Put Away,Storage Function,Shipping Function (order fulfillment),Load,Pack,Order Pick,Storage Function,Storage Function,Single Factor Productivity Metrics,Productivity =,Traditional Performance Metrics,Fill rate Inventory turns Lines/hour Orders/hour $/

3、line $/order,Work ok when,Requirements are not changing Technology is not changing Competition is not changing,Its very hard to interpret a single factor productivity metric when the environment is subject to rapid change in products, customer requirements, technology, or competition.,But in a dynam

4、ic world ,Cant compare over time Cant compare across locations Cant compare to other companies At least not without a lot of additional explanatory data and information!,Benchmarking,Relative performance level Best (effective) practices,INPUTS,OUTPUTS,System-oriented Performance Measure,The Need,Res

5、ources,Services,Activities,Total Factor Productivity?,Cant solve the pricing problem,INPUTS,OUTPUTS,ONE PERFORMANCE INDEX,Data Envelopment Analysis,Resources,Services,Activities,System-based assessment method,Resources: capital, labor, overhead Activities: inbound, order fulfillment Services: lines/

6、qty shipped, fill rate, etc,Compare to other warehouses,All other warehouses All other warehouses in your industry All other warehouses in your company Your warehouse in the past,Production Function Theoryfor one input, one output,Resource/Input,Production/Output,System Efficiency Concept,Resource/I

7、nput,Production/Output,O,B,A,System efficiency of warehouse B is the ratio OA OB,DEA Model: Charnes, Cooper, and Rhodes,Constant Returns to Scale,Data Envelopment Analysis,Allows us to consider multiple “inputs” Allows us to consider multiple “outputs” Determines the reference point on the productio

8、n function by constructing a hypothetical “best practices” warehouse using real warehouse data Best possible* not average* * from data,DEA Performance Score,Contribution to Profit,Input/Output Specification(the Frazelle/Hackman model),Efficiency,Warehouse,Lines Shipped,Storage Function,Accumulation,

9、Total Staffing,Equipment “Replacement” Cost,Warehouse area,Html documents,Solver,Database,At your site,GT Server,Web-based Tool,Over the internet,Over 150 qualified users,Results to Date,Experience,Existing database More than 150 warehouses Not segmented by industry (yet) No “descriptive” data to us

10、e for segmenting Can segment based on inputs and outputs,Output Segmentation,broken case: 49 full case: 32 pallet: 13 mix: 65 total: 159,Input-oriented, all 159 together,Broken Case, Input EfficiencyCompared to All (49/159),Broken Case, Input EfficiencyCompared Within (49/49),Pick Rate for Broken Ca

11、se Picking,Ave = 17SD = 27,Full case, Input Efficiency Compared to All (32/159),Full case, Input Efficiency Compared Within (32/32),Pick Rate for Case Picking,Ave = 14SD = 27.7,Pallet, Input Efficiency Compared to all (13/159),Pallet, Input Efficiency Compared within (13/13),Pick Rate for Pallet Pic

12、king,Lines/Labor hour ( pallet),0,2,4,6,8,10,0.0,10.0,20.0,30.0,40.0,50.0,100.0,150.0,200.0,More,lines/labor hour,Frequency,Ave = 25SD = 27.7,Mixed, Input Efficiency Compared to all (65/159),Mixed, Input Efficiency Compared Within (65/65),Pick Rate for Mixed Picking,Lines/Labor Hour (mix),0,10,20,30

13、,40,50,60,0.00,20.00,40.00,60.00,80.00,100.00,lines/labor hour,Frequency,Ave = 10.6SD = 23,Aggregate Pick Rate for All 159,Where do we go from here?,Many Opportunities to Improve the Benchmarking Tool,Enhance the basic input/output model Enhance the ability to benchmark for technology, practice, & r

14、equirements,Some Suggested Metrics,Inputs Space Capital Labor Inventory # of skus turns,Outputs Inbound receipt mix receipt variability returns time to availability Fulfillment pick volume pick variability pick accuracy fill rate, but, Sorta ,“Marker” Analysis,Performance “Marker” Attribute,DEA Perf

15、ormance Score,Performance “Marker” Practice,DEA Performance Score,Results,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. Labor hours was not found to be a significant factor, by i

16、tself, 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 di

17、fferences becomes less prominent the higher the labor hours).,More Results,The interaction of investment and area was found to be significant. This means that high investment warehouses are even less efficient if they are also large. No matter how we segment the data, a very large proportion of ware

18、houses are operating at or below 50% system efficiency. While this may reflect seasonal fluctuations in customer orders, it still represents a very significant opportunity for improvement.,More Results,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.,Other Data Requirements,Type (wholesale, retail, manufacturing) Industry (pharma, au