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Transcript of 1 Outline ideas of benchmarking DEA profiling. 2 Purpose of the Course warehouses and warehousing:...
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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