2 Purpose of the Course warehouses and warehousing: means, not ends ends for studentssatisfy the course requirementprepare for thesishow to collect information, present, write an essayself-improve and self-actualize
3 Thesisa serious issuecertainly not something from cutting and pastingnot merely a collection of organized materiala step on generating knowledgematerial read serving as the basiskey: your own thoughtshard, but worthwhile training
4 Term Project the training for your thesis just try your best, and don’t worry that much
6 Tasks for Senior Management of Warehouses continuous improvementsetting objectivesabsolute standard, e.g., 95% orders in 2 days, on average no more than 2.2 daysrelative standard – benchmarkingprofiling: pre-requisite of benchmarking“soul” searching
7 Steps for Benchmarking identify the process to benchmark for e.g., most troublesome, most importantidentify the key performance variables: efficiency (time, cost, productivity) and service leveldocument current processes and flows: physical activities and information flowsincluding resources requiredidentify competitors and best-in-class companiesdecide which practices to adoptsee modifications
8 Data Collected for Benchmarking Warehouses performance benchmarkinginputs, e.g.,labor, investment, space, scale of storage, degree of automationoutputs# of lines picked, level of value added service, # of special processes, quality of service, flexibility of servicebroken case lines shipped, full case lines shipped and pallet lines shippedprocess benchmarkingresourcesprocedureresults
9 Difficulties of Benchmarking intangible factorshow to measure factors such as degree of automation, level of value added service, quality of service, flexibility of service, etc.incomparable factorse.g., the comparison of quality of service with degree of automation
10 Common Approaches for Intangible Factors qualitative description, e.g.,different levels of sophistication of receivingStage 1measureStage 3Stage 4Stage 5Receivingunload, stage, & in-checkimmediate putaway to reserveimmediate putaway to primarycross-dockingprereceiving
12 Common Approaches for Intangible Factors numerical values assigned to qualitative factorsquantitative measures for qualitative factorse.g., quality of service by % of customers satisfied in 5 minutes, level of value added service by types of value added service provided
13 Examples of Numerical Performance Indicators Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))FinancialProductivityUtilizationQualityCycle timeReceivingPutawayStorageOrder pickingShippingTotal
14 Examples of Numerical Performance Indicators Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))FinancialProductivityUtilizationQualityCycle timeReceivingCost / lineReceipts/ man-hrDock utilization% of correct receiptsprocessing time / receiptPutawayCost /lineLabor & equipment utilization% of perfect putawayCycle time / putawayStorageCost / itemInv / areaSpace utilization% of accurate recordInv. dayOrder pickingLine picked% of correct picked linesPick cycle timeShippingCost / orderOrder shipped% of perfect shipmentscycle time / orderTotalCost / order, line, itemLines shipped---% of perfect W/H ordersCycle time / order
15 Presenting Incomparable Factors degree of automationflexibility of servicelevel of value added servicequality of servicescale of operationstraining of personnelskipping comparison, e.g., the web graph for gap analysisan example for 6 factorsbest practices identified for benchmarkingthe relative performance with respect to the best praes
16 Comparing Incomparable Factors various methods, e.g., Scoring, Analytic Hierarchy Process, Balanced Scorecard, Data Envelopment Analysis (DEA), etc.
18 Comparing Incomparable Factors data envelopment analysis (DEA): a technique to compare quantitative factors of different natureproviding a numerical value judging the distance from the best practicessome assumptionsnumerical values of each factor, e.g., input1 = 5, input2 = 12, though input1 and input2 cannot be comparedlinearity of effect, i.e., if 3 units of input give 7 units of outputs, 6 units of input give 14 units of output
19 Idea of Data Envelopment Analysis (DEA) W/H A and W/H B consume the same amount of resourcestwo types of incomparable outputs: apple and orangewhich is better?appleorangeA (4, 8)B (8, 4)
20 Idea of Data Envelopment Analysis (DEA) W/H C consumes the same amount of resources as W/Hs A and B doHow’s the performance of C relative to A and B?appleorangeA (4, 8)B (8, 4)C (8, 8)C (6, 6)C (4, 4)
21 Idea of Data Envelopment Analysis (DEA) Given W/H A and B, for W/Hs that consumes the same amount of resources, the inefficient region is shown in RHS.The efficiency of a warehouse that consumes the same amount of resources as A and B can be measured by the distance from the boundary of the date envelope.appleorangeABinefficient regionmeasurement of inefficiency
22 Idea of Data Envelopment Analysis (DEA) efficient boundary from many warehouses that consume the same amount of resourcesappleorangeinefficient region
23 Idea of Data Envelopment Analysis (DEA) efficient boundary from many warehouses that give the same amount of outputs and consume different values of incomparable resources banana and grapefruitbananagrapefruitinefficient region
24 Idea of Data Envelopment Analysis (DEA) problem: situations for benchmarking often not idealdifferent resources consumption for W/Hdifferent outputs for W/Hfor multi-input, multi-output problems, with W/H consuming different amount of resources and giving different amount of outputs, DEAdraws the efficient boundarybenchmarks a W/H with respect to these existing ones
25 Idea of Data Envelopment Analysis (DEA) multi-input, multi-output comparisonI decision-making units (DMUs), J types of inputs, K types of outputsaij be the number of units of input j that entity i takes to give aik units of output k, j = 1, …, J and k = J+1, …, J+Kexample: 2 DMUs; 2 types of inputs (grapefruit, banana); 2 types of outputs (apple, orange)DMU 1: a11 = 1, a12 = 3, a13 = 5, and a14 = 2, i.e., DMU 1 takes 1 grapefruit, 3 bananas to produce 5 apples and 2 orangesDMU 2: a21 = 2, a22 = 1, a23 = 3, and a24 = 4, i.e., DMU 2 takes 2 grapefruits, 1 banana to produce 3 apples and 4 oranges
26 Idea of Data Envelopment Analysis (DEA) rk = unit reward of type k output, cj = unit cost of type j inputperformance of DMU 1 = (5r3+2r4)/(c1+3c2)performance of DMU 2 = (3r3+4r4)/(2c1+c2)performance of DMU i defined similarlygiven (aij) of the I DMUs, how to benchmark a tapped DMU with (aoj) for unknown rk and cj?
27 Idea of Data Envelopment Analysis (DEA) in general DEA finds the distance from the efficient boundary by a linear program purely making use of (aij) and (aoj) without knowing rk, nor cjidea: similar to the construction of efficient boundaries in the simplified examples
28 Studies Using DEA on Warehouses de Koster, M.B.M., and B.M. Balk (2008) Benchmarking and Monitoring International Warehouse Operations in Europe, Production and Operations Management, 17(2),McGinnis, L.F., A. Johnson, and M. Villarreal (2006) Benchmarking Warehouse Performance Study, Technical Report, Georgia Institute of Technology.
29 de Koster and Balk (2008) inputs # of direct FTEs size of the W/H degree of automation# of SKUsoutputs# of order lines picked/daylevel of value-added logistics (VAL) activities# of special optimized processes% of error-free orders shipped outorder flexibility
30 de Koster and Balk (2008) 65 warehouses containing 140 EDCs EDC: distribution centers in Europe responsible for the distribution for at least five countries therecompositionresults
31 Warehouse Performance Study in GIT develop a single index to measure the performance of a warehouseuse data envelope analysis
32 Examples from the Index – Warehouse Size What are your inferences?
33 Examples from the Index – Mechanization What are your inferences?
35 Profiling profile of the warehouse define processes status of processesreveal status of warehousepurposesget new ideas on design and planningget improvementget baseline for any justificationremarksuse distributions, not meansexpress in pictures
36 Various Profiles indicators on every aspect receiving, prepackaging, putaway, storage, order picking, packaging, sorting, accumulation, unitizing, and shipping
37 Customer Order Profiling Customer Order ProfileOrder Mix Dist.Lines per order Dist.Lines and Cube per order Dist.Cube per order Dist.Family Mix Dist.Full/Partial Mix Dist.Order Inc. Dist.results from order profiling help design a warehouse, including its layout, equipment, picking methods, etc.
38 Family Mix Distribution implication: zoning by family
39 Handling Unit Mix Distribution – Full/Partial Pallets implication: good to have a separate picking area for loose cartons
40 Handling Unit Mix Distribution – Full/Broken Cases implication: good to have a separate picking area for broken cases
41 Order Increment Distributions - Pallets implication: good to have ¼ and ½ pallets
42 Order Increment Distributions - Cases implication: good to have ½-size cases
43 Lines per order Distribution implication: on the picking methods
44 Lines and Cube per order Distribution implication: on the picking methods
45 Items Popularity Distribution implication: on storage zones, golden, silver, bronze
46 Cube-Movement Distribution implication: small items in drawers or bin shelling; large items in block stacking, push-back rack
47 Popularity-Cube-Movement Distribution implication: on storage mode
48 Item-Order Completion Distribution implication: on mode of storage, e.g., warehouse within a warehouse
49 Demand Correlation Distribution implication: on zoning of goods
50 Demand Variability Distribution implication: variance of demand to set safety stock
51 Item-Family Inventory Distribution implication: area assigned to different types of storage
52 Handling Unit Inventory Distribution implication: different storage modes according to the number of pallets on hand
53 Seasonality Distribution implication: shifting human resources and possibly space
54 Daily Activity Distribution implication: shifting human resources and possibly space