美国某公司 仓库管理系统课件

1、Internet-basedData Envelopment Analysisfor WarehousingOutlineThe problemThe current solutionThe needA new solutionHow it worksInternet deployment and results to dateFuture directionsPerformance AssessmentHow well are you performing?Do you have opportunities to improve?Single Factor Productivity Metr

2、icsProductivity =outputinputTraditional Performance MetricsFill rateInventory turnsLines/hourOrders/hour$/line$/orderWork ok whenRequirements are not changingTechnology is not changingCompetition is not changingIts very hard to interpret a single factor productivity metric when the environment is su

3、bject to rapid change in products, customer requirements, technology, or competition.But in a dynamic world Cant compare over timeCant compare across locationsCant compare to other companiesAt least not without a lot of additional explanatory data and information!INPUTSOUTPUTSSystem-oriented Perform

4、ance MeasureThe NeedResourcesServicesActivitiesINPUTSOUTPUTSONEPERFORMANCE INDEXData Envelopment AnalysisResourcesServicesActivitiesCompare to other warehousesAll other warehousesAll other warehouses in your industryAll other warehouses in your companyYour warehouse in the pastProduction Function Th

5、eoryfor one input, one outputResource/InputProduction/OutputSystem Efficiency ConceptResource/InputProduction/OutputOBASystem efficiency of warehouse B is the ratioOAOBDEA Model: Charnes, Cooper, and RhodesConstant Returns to ScaleData Envelopment AnalysisAllows us to consider multiple “inputs”Allow

6、s us to consider multiple “outputs”Determines the reference point on the production function by constructing a hypothetical “best practices” warehouse using real warehouse dataBest possible* not average* from dataInput/Output Specification(the Frazelle/Hackman model)EfficiencyWarehouseLines ShippedS

7、torage FunctionAccumulationTotal StaffingEquipment “Replacement” CostWarehouse areaHtmldocumentsSolverDatabaseAt your siteGT ServerWeb-based ToolOver the internetResults to DateExperienceExisting databaseMore than 150 warehousesNot segmented by industry (yet)No “descriptive” data to use for segmenti

8、ngCan segment based on inputs and outputsOutput Segmentationbroken case: 49full case: 32pallet: 13mix: 65total: 159Input-oriented, all 159 togetherBroken Case, Input EfficiencyCompared to All (49/159)Broken Case, Input EfficiencyCompared Within (49/49)Pick Rate for Broken Case PickingAve = 17SD = 27

9、Full case, Input Efficiency Compared to All (32/159)Full case, Input Efficiency Compared Within (32/32)Pick Rate for Case PickingAve = 14SD = 27.7Pallet, Input Efficiency Compared to all (13/159)Pallet, Input Efficiency Compared within (13/13)Pick Rate for Pallet PickingLines/Labor hour ( pallet)024

10、68100.010.020.030.040.050.0100.0150.0200.0Morelines/labor hourFrequencyAve = 25SD = 27.7Mixed, Input Efficiency Compared to all (65/159)Mixed, Input Efficiency Compared Within (65/65)Pick Rate for Mixed PickingLines/Labor Hour (mix)01020304050600.0020.0040.0060.0080.00100.00lines/labor hourFrequency

11、Ave = 10.6SD = 23Aggregate Pick Rate for All 159Where do we go from here?Many Opportunities to Improve the Benchmarking ToolEnhance the basic input/output modelEnhance the ability to benchmark for technology, practice, & requirementsSome Suggested MetricsInputsSpaceCapitalLaborInventory# of skusturn

12、sOutputsInboundreceipt mixreceipt variabilityreturnstime to availabilityFulfillmentpick volumepick variabilitypick accuracyfill rate but Sorta “Marker” AnalysisPerformance “Marker” AttributeDEA Performance ScorePerformance “Marker” PracticeDEA Performance ScoreResultsBigger is not always better, at

13、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 investment was found to be

14、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). More ResultsThe interaction of investment an

15、d 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 this may reflect seasonal fluctuations in cus

16、tomer orders, it still represents a very significant opportunity for improvement. More ResultsThe 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 seg

17、ment, and a larger proportion are operating below 50% efficiency. Other Data RequirementsType (wholesale, retail, manufacturing)Industry (pharma, auto, electronics, )Order volumeSku spreadOrder size distributionPallet/case/each distributionPlanning horizonDegree of automationPush vs PullPicking strategy Sorta Still More Data RequirementsPracticesWMS?Compliant shipping?Space utilization?Velocity based slottingetcDiagnosticscost/carton shippedvalue added servicesinventory