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OutlineOutline

ideas of benchmarking DEA profiling

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Purpose of the CoursePurpose of the Course

warehouses and warehousing: means, not ends

ends for students satisfy the course requirement

prepare for thesis how to collect information, present, write an essay

self-improve and self-actualize

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Thesis Thesis

a serious issue

certainly not something from cutting and pasting

not merely a collection of organized material

a step on generating knowledge

material read serving as the basis

key: your own thoughts

hard, but worthwhile training

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Term Project Term Project

the training for your thesis

just try your best, and don’t worry that

much

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Benchmarking and ProfilingBenchmarking and Profiling

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Tasks for Tasks for Senior Management of WarehousesSenior Management of Warehouses

continuous improvement setting objectives

absolute standard, e.g., 95% orders in 2 days, on average no more than 2.2 days

relative standard – benchmarking profiling: pre-requisite of benchmarking

“soul” searching

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Steps for BenchmarkingSteps for Benchmarking

identify the process to benchmark for e.g., most troublesome, most important

identify the key performance variables: efficiency (time, cost, productivity) and service level

document current processes and flows: physical activities and information flows including resources required

identify competitors and best-in-class companies decide which practices to adopt

see modifications

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Data Collected Data Collected for Benchmarking Warehousesfor Benchmarking Warehouses

performance benchmarking inputs, e.g.,

labor, investment, space, scale of storage, degree of automation

outputs # of lines picked, level of value added service, # of special processes,

quality of service, flexibility of service broken case lines shipped, full case lines shipped and pallet lines shipped

process benchmarking resources procedure results

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Difficulties of BenchmarkingDifficulties of Benchmarking

intangible factors how to measure factors such as degree of

automation, level of value added service, quality of service, flexibility of service, etc.

incomparable factors e.g., the comparison of quality of service with

degree of automation

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Common Approaches Common Approaches for Intangible Factors for Intangible Factors

qualitative description, e.g., different levels of sophistication of receiving

Stage 1 measure Stage 3 Stage 4 Stage 5

Receivingunload, stage, &

in-checkimmediate putaway

to reserve immediate putaway

to primarycross-docking prereceiving

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Steps to World-Class Steps to World-Class Warehousing PracticesWarehousing Practices

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Common Approaches Common Approaches for Intangible Factors for Intangible Factors

numerical values assigned to qualitative factors

quantitative measures for qualitative factors e.g., quality of service by % of customers

satisfied in 5 minutes, level of value added service by types of value added service provided

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Examples of Examples of Numerical Performance IndicatorsNumerical Performance Indicators

Financial Productivity Utilization Quality Cycle time

Receiving

Putaway

Storage

Order picking

Shipping

Total

Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))

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Examples of Examples of Numerical Performance IndicatorsNumerical Performance Indicators

Financial Productivity Utilization Quality Cycle time

Receiving Cost / lineReceipts / man-hr

Dock utilization% of correct

receiptsprocessing time

/ receipt

Putaway Cost /linePutaway / man-hr

Labor & equipment utilization

% of perfect putaway

Cycle time / putaway

Storage Cost / item Inv / area Space utilization% of accurate

recordInv. day

Order picking

Cost / lineLine picked

/ man-hrLabor & equipment

utilization% of correct picked lines

Pick cycle time

Shipping Cost / orderOrder shipped

/ man-hrDock utilization

% of perfect shipments

cycle time / order

TotalCost / order,

line, itemLines shipped

/ man-hr---

% of perfect W/H orders

Cycle time / order

Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))

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PresentingPresentingIncomparable Factors Incomparable Factors

skipping comparison, e.g., the web graph for gap analysis an example for 6 factors

best practices identified for benchmarking

the relative performance with respect to the best praes

degree of automation

flexibility of service

level of value added service

quality of service

scale of operations

training of personnel

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ComparingComparingIncomparable Factors Incomparable Factors

various methods, e.g., Scoring, Analytic Hierarchy Process, Balanced Scorecard, Data Envelopment Analysis (DEA), etc.

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Data Envelopment Analysis Data Envelopment Analysis (DEA)(DEA)

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Comparing Comparing Incomparable Factors Incomparable Factors

data envelopment analysis (DEA): a technique to compare quantitative factors of different nature

providing a numerical value judging the distance from the best practices

some assumptions numerical values of each factor, e.g., input1 = 5, input2 =

12, though input1 and input2 cannot be compared

linearity of effect, i.e., if 3 units of input give 7 units of outputs, 6 units of input give 14 units of output

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Idea of Idea of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA)

W/H A and W/H B consume the same amount of resources

two types of incomparable outputs: apple and orange

which is better?

A (4, 8)

B (8, 4)

apple

orange

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Idea of Idea of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA)

W/H C consumes the same amount of resources as W/Hs A and B do

How’s the performance of C relative to A and B? A (4, 8)

B (8, 4)

apple

orange

C (4, 4)

C (8, 8)

C (6, 6)

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Idea of Idea of Data Envelopment Analysis (DEA) 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.

apple

orange

A

B inefficient

region

measurement of inefficiency

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Idea of Idea of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA)

efficient boundary from many warehouses that consume the same amount of resources

inefficient region

apple

orange

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Idea of Idea of Data Envelopment Analysis (DEA) 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 grapefruit

banana

grapefruit

inefficient region

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Idea of Idea of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA) problem: situations for benchmarking often not ideal

different resources consumption for W/H

different outputs for W/H

for multi-input, multi-output problems, with W/H consuming different amount of resources and giving different amount of outputs, DEA draws the efficient boundary

benchmarks a W/H with respect to these existing ones

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Idea of Idea of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA) multi-input, multi-output comparison

I decision-making units (DMUs), J types of inputs, K types of outputs

aij 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+K

example: 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 oranges

DMU 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

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Idea of Idea of Data Envelopment Analysis (DEA) Data Envelopment Analysis (DEA)

rk = unit reward of type k output, cj = unit cost of type j input

performance of DMU 1 = (5r3+2r4)/(c1+3c2)

performance of DMU 2 = (3r3+4r4)/(2c1+c2)

performance of DMU i defined similarly

given (aij) of the I DMUs, how to benchmark a tapped DMU with (aoj) for unknown rk and cj?

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Idea of Idea of Data Envelopment Analysis (DEA) 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 cj

idea: similar to the construction of efficient

boundaries in the simplified examples

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Studies Using DEA on WarehousesStudies 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), 175-183.

McGinnis, L.F., A. Johnson, and M. Villarreal (2006) Benchmarking Warehouse Performance Study, Technical Report, Georgia Institute of Technology.

de Koster and Balk (2008)de Koster and Balk (2008)

inputs

# of direct FTEs

size of the W/H

degree of

automation

# of SKUs

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outputs

# of order lines picked/day

level of value-added logistics (VAL) activities

# of special optimized processes

% of error-free orders shipped out

order flexibility

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de Koster and Balkde Koster and Balk (2008) (2008)

65 warehouses containing 140 EDCs

EDC: distribution centers in Europe responsible for the distribution for at least five countries there

composition

results

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Warehouse Performance Study Warehouse Performance Study in GITin GIT

develop a single index to measure the performance of a warehouse

use data envelope analysis

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Examples from the Index Examples from the Index –– Warehouse SizeWarehouse Size

What are your inferences?

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Examples from the Index Examples from the Index –– MechanizationMechanization

What are your inferences?

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ProfilingProfiling Examples Only Examples Only

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ProfilingProfiling profile of the warehouse

define processes

status of processes

reveal status of warehouse

purposes get new ideas on design and planning

get improvement

get baseline for any justification

remarks use distributions, not means

express in pictures

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Various ProfilesVarious Profiles

indicators on every aspect receiving, prepackaging, putaway, storage, order picking,

packaging, sorting, accumulation, unitizing, and shipping

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Customer Order ProfilingCustomer Order Profiling

Family Mix Dist.

Full/Partial Mix Dist.

Order Inc. Dist.

Order Mix Dist. Lines per order Dist.

Lines and Cube per order Dist.

Cube per order Dist.

results from order profiling help design a

warehouse, including its layout, equipment,

picking methods, etc.

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Family Mix DistributionFamily Mix Distribution

implication: zoning by family

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Handling Unit Mix Distribution Handling Unit Mix Distribution –– Full/Partial Pallets Full/Partial Pallets

implication: good to have a separate picking area for loose cartons

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Handling Unit Mix Distribution Handling Unit Mix Distribution –– Full/Broken Cases Full/Broken Cases

implication: good to have a separate picking area for broken cases

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Order Increment Distributions Order Increment Distributions - Pallets- Pallets

implication: good to have ¼ and ½ pallets

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Order Increment Distributions - Order Increment Distributions - CasesCases

implication: good to have ½-size cases

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Lines per order DistributionLines per order Distribution

implication: on the picking methods

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Lines and Cube per order Lines and Cube per order DistributionDistribution

implication: on the picking methods

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Items Popularity DistributionItems Popularity Distribution

implication: on storage zones, golden, silver, bronze

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Cube-Movement DistributionCube-Movement Distribution

implication: small items in drawers or bin shelling; large items in block stacking, push-back rack

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Popularity-Cube-Movement Popularity-Cube-Movement DistributionDistribution

implication: on storage mode

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Item-Order Completion Item-Order Completion DistributionDistribution

implication: on mode of storage, e.g., warehouse within a warehouse

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Demand Correlation DistributionDemand Correlation Distribution

implication: on zoning of goods

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Demand Variability DistributionDemand Variability Distribution

implication: variance of demand to set safety stock

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Item-Family Inventory Item-Family Inventory DistributionDistribution

implication: area assigned to different types of storage

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Handling Unit Inventory Handling Unit Inventory DistributionDistribution

implication: different storage modes according to the number of pallets on hand

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Seasonality DistributionSeasonality Distribution

implication: shifting human resources and possibly space

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Daily Activity DistributionDaily Activity Distribution

implication: shifting human resources and possibly space

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Activity RelationshipActivity Relationship

implication: on layout