美国的一个仓库管理系统（PPT63页）

1、 2002 Georgia TechInternet-basedData Envelopment Analysisfor Warehousing 2002 Georgia TechOutline The problem The current solution The need A new solution How it works Internet deployment and results to date Future directions 2002 Georgia TechPerformance Assessment How well are you performing? Do yo

2、u have opportunities to improve? 2002 Georgia TechWarehouse OperationsReceiving Function (inbound)UnloadInspectPut AwayStorageFunctionShipping Function (order fulfillment)LoadPackOrder PickStorageFunctionStorageFunction 2002 Georgia TechSingle Factor Productivity MetricsProductivity =outputinput 200

3、2 Georgia TechTraditional Performance Metrics Fill rate Inventory turns Lines/hour Orders/hour $/line $/order 2002 Georgia TechWork ok when Requirements are not changing Technology is not changing Competition is not changingIts very hard to interpret a single factor productivity metric when the envi

4、ronment is subject to rapid change in products, customer requirements, technology, or competition. 2002 Georgia TechBut in a dynamic world Cant compare over time Cant compare across locations Cant compare to other companiesAt least not without a lot of additional explanatory data and information! 20

5、02 Georgia TechBenchmarkingRelative performance levelBest (effective) practices 2002 Georgia TechINPUTSOUTPUTSSystem-oriented Performance MeasureThe NeedResourcesServicesActivities 2002 Georgia TechTotal Factor Productivity?Cant solve the pricing problem 2002 Georgia TechINPUTSOUTPUTSONEPERFORMANCE

6、INDEXData Envelopment AnalysisResourcesServicesActivities 2002 Georgia TechSystem-based assessment method Resources: capital, labor, overhead Activities: inbound, order fulfillment Services: lines/qty shipped, fill rate, etc 2002 Georgia TechCompare to other warehousesAll other warehousesAll other w

7、arehouses in your industryAll other warehouses in your companyYour warehouse in the past 2002 Georgia TechProduction Function Theoryfor one input, one outputResource/InputProduction/Output 2002 Georgia TechSystem Efficiency ConceptResource/InputProduction/OutputOBASystem efficiency of warehouse B is

8、 the ratioOAOB 2002 Georgia TechDEA Model: Charnes, Cooper, and Rhodesssss11min,. .ts00sXX0YsY0,ssConstant Returns to Scale 2002 Georgia TechData Envelopment Analysis Allows us to consider multiple “inputs” Allows us to consider multiple “outputs” Determines the reference point on the production fun

9、ction by constructing a hypothetical “best practices” warehouse using real warehouse data Best possible* not average* from data 2002 Georgia TechDEA Performance ScoreContribution to Profit 2002 Georgia TechInput/Output Specification(the Frazelle/Hackman model)EfficiencyWarehouseLines ShippedStorage

10、FunctionAccumulationTotal StaffingEquipment “Replacement” CostWarehouse area 2002 Georgia TechHtmldocumentsSolverDatabaseAt your siteGT ServerWeb-based ToolOver the internet 2002 Georgia TechOver 150 qualified users 2002 Georgia TechResults to Date 2002 Georgia TechExperience Existing database More

11、than 150 warehouses Not segmented by industry (yet) No “descriptive” data to use for segmenting Can segment based on inputs and outputs 2002 Georgia TechOutput Segmentation broken case: 49 full case: 32 pallet: 13 mix: 65 total: 159 2002 Georgia TechInput-oriented, all 159 together0.00.51.001020all_

12、VRS_IFrequency 2002 Georgia TechBroken Case, Input EfficiencyCompared to All (49/159)0.10.20.30.40.50.60.70.80.91.00510B-VRS-IFrequency 2002 Georgia TechBroken Case, Input EfficiencyCompared Within (49/49)0.10.20.30.40.50.60.70.80.91.001020brok_I_vrsFrequency 2002 Georgia TechPick Rate for Broken Ca

13、se PickingAve = 17SD = 27 2002 Georgia TechFull case, Input Efficiency Compared to All (32/159)0.10.20.30.40.50.60.70.80.91.00510F-VRS-IFrequency 2002 Georgia TechFull case, Input Efficiency Compared Within (32/32)0.10.20.30.40.50.60.70.80.91.0012345678full_I_vrsFrequency 2002 Georgia TechPick Rate

14、for Case PickingAve = 14SD = 27.7 2002 Georgia TechPallet, Input Efficiency Compared to all (13/159)0.20.30.40.50.60.70.80.91.0012345P-VRS-IFrequency 2002 Georgia TechPallet, Input Efficiency Compared within (13/13)0.40.50.60.70.80.91.00510plt_I_vrsFrequency 2002 Georgia TechPick Rate for Pallet Pic

15、kingLines/Labor hour ( pallet)02468100.010.020.030.040.050.0100.0150.0200.0Morelines/labor hourFrequencyAve = 25SD = 27.7 2002 Georgia TechMixed, Input Efficiency Compared to all (65/159)0.00.10.20.30.40.50.60.70.80.91.0051015M-VRS-IFrequency 2002 Georgia TechMixed, Input Efficiency Compared Within

16、(65/65)0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.0501020mix_I_vrsFrequency 2002 Georgia TechPick Rate for Mixed PickingLines/Labor Hour (mix)01020304050600.0020.0040.0060.0080.00100.00lines/labor hourFrequencyAve = 10.6SD = 23 2002 Georgia Tech051015202530354024681012141618202224262830MoreLines

17、 per HourFrequencyAverageFigure 6. Distribution of Lines per Labor Hour051015202530354024681012141618202224262830MoreLines per HourFrequencyAverageFigure 6. Distribution of Lines per Labor HourAggregate Pick Rate for All 159 2002 Georgia TechWhere do we go from here? 2002 Georgia TechMany Opportunit

18、ies to Improve the Benchmarking Tool Enhance the basic input/output model Enhance the ability to benchmark for technology, practice, & requirements 2002 Georgia TechSome Suggested MetricsInputs Space Capital Labor Inventory # of skus turnsOutputs Inbound receipt mix receipt variability returns t

19、ime to availability Fulfillment pick volume pick variability pick accuracy fill rate but Sorta 2002 Georgia Tech“Marker” Analysis 2002 Georgia TechPerformance “Marker” AttributeDEA Performance Score 2002 Georgia TechPerformance “Marker” PracticeDEA Performance Score 2002 Georgia TechResults Bigger i

20、s 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 itself, in predicting system efficiency. However, the interaction of labor with inv

21、estment 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). 2002 Georgia TechMor

22、e 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 warehouses are operating at or below 50% system efficiency. While thi

23、s may reflect seasonal fluctuations in customer orders, it still represents a very significant opportunity for improvement. 2002 Georgia TechMore 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% efficien