Transcript Intelligent Management of Container Terminals Chuqian Zhang

Outline
 ideas
of benchmarking
 DEA
 profiling
 1 
Purpose of the Course
 warehouses
and warehousing: means, not
ends
 ends
for students
 satisfy
the course requirement
 prepare
 how
for thesis
to collect information, present, write an essay
 self-improve
and self-actualize
 2 
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
 3 
Term Project
 the
training for your thesis
 just
try your best, and don’t worry that
much
 4 
Benchmarking and Profiling
 5 
Tasks for
Senior 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:
 “soul”
pre-requisite of benchmarking
searching
 6 
Steps 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
 7 
Data Collected
for 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
 8 
Difficulties 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
 9 
Common Approaches
for Intangible Factors
 qualitative

Receiving
description, e.g.,
different levels of sophistication of receiving
Stage 1
measure
Stage 3
Stage 4
Stage 5
unload, stage, &
in-check
immediate putaway
to reserve
immediate putaway
to primary
cross-docking
prereceiving
 10 
Steps to World-Class
Warehousing Practices
 11 
Common Approaches
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
 12 
Examples of
Numerical Performance Indicators
Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))
Financial
Productivity
Utilization
Receiving
Putaway
Storage
Order
picking
Shipping
Total
 13 
Quality
Cycle time
Examples of
Numerical Performance Indicators
Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))
Financial
Productivity
Utilization
Quality
Cycle time
Receiving
Cost / line
Receipts
/ man-hr
Dock utilization
% of correct
receipts
processing time
/ receipt
Putaway
Cost /line
Putaway
/ man-hr
Labor & equipment
utilization
% of perfect
putaway
Cycle time /
putaway
Storage
Cost / item
Inv / area
Space utilization
% of accurate
record
Inv. day
Order
picking
Cost / line
Line picked
/ man-hr
Labor & equipment
utilization
% of correct
picked lines
Pick cycle time
Shipping
Cost / order
Order shipped
/ man-hr
Dock utilization
% of perfect
shipments
cycle time /
order
Total
Cost / order,
line, item
Lines shipped
/ man-hr
---
% of perfect
W/H orders
Cycle time /
order
 14 
Presenting
Incomparable Factors
scale of
operations
 skipping
comparison, e.g.,
the web graph for gap
analysis
 an
degree of
automation
quality of
service
flexibility
of service
example for 6 factors
 best
practices identified for
benchmarking
 the
relative performance with
respect to the best praes
 15 
training of
personnel
level of value
added service
Comparing
Incomparable Factors
 various
methods, e.g., Scoring, Analytic
Hierarchy Process, Balanced Scorecard,
Data Envelopment Analysis (DEA), etc.
 16 
Data Envelopment Analysis
(DEA)
 17 
Comparing
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
 18 
Idea of
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
A (4, 8)
 which
is better?
orange
B (8, 4)
 19 
apple
Idea of
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)
orange
C (8, 8)
C (6, 6)
C (4, 4)
 20 
B (8, 4)
apple
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.
orange

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.
 21 
A
measurement
of inefficiency
inefficient
region
apple
B
Idea of
Data Envelopment Analysis (DEA)
 efficient
boundary from many warehouses
that consume the same amount of resources
orange
inefficient
region
apple
 22 
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 grapefruit
banana
 23 
inefficient
region
grapefruit
Idea of
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
 24 
Idea of
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
 25 
Idea of
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?
 26 
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 cj
 idea:
similar to the construction of efficient
boundaries in the simplified examples
 27 
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),
175-183.

McGinnis, L.F., A. Johnson, and M. Villarreal (2006)
Benchmarking Warehouse Performance Study, Technical
Report, Georgia Institute of Technology.
 28 
de Koster and Balk (2008)

 inputs
#
of direct FTEs
 size

# of order lines picked/day

level of value-added
logistics (VAL) activities

# of special optimized
processes

% of error-free orders
shipped out

order flexibility
of the W/H
 degree
of
automation
#
outputs
of SKUs
 29 
de Koster and Balk (2008)

65 warehouses containing 140 EDCs

EDC: distribution centers in Europe responsible for the distribution
for at least five countries there

composition

results
 30 
Warehouse Performance Study
in GIT

develop a single index to measure the performance of
a warehouse

use data envelope analysis
 31 
Examples from the Index –
Warehouse Size
 What
are your inferences?
 32 
Examples from the Index –
Mechanization
 What
are your inferences?
 33 
Profiling
 Examples Only
 34 
Profiling

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
 35 
Various Profiles

indicators on every aspect

receiving, prepackaging, putaway, storage, order picking,
packaging, sorting, accumulation, unitizing, and shipping
 36 
Customer Order Profiling
Order Mix Dist.
Family Mix
Dist.
Full/Partial
Mix Dist.
Lines per
order Dist.
Order Inc.
Dist.
 37 
Cube per
order Dist.
Lines and Cube
per order Dist.
results from order
profiling help design a
warehouse, including its
layout, equipment,
picking methods, etc.
Family Mix Distribution
 implication:
zoning by family
 38 
Handling Unit Mix Distribution
– Full/Partial Pallets
 implication:
good to have a separate picking
area for loose cartons
 39 
Handling Unit Mix Distribution
– Full/Broken Cases
 implication:
good to have a separate picking
area for broken cases
 40 
Order Increment Distributions
- Pallets
 implication:
good to have ¼ and ½ pallets
 41 
Order Increment Distributions Cases
 implication:
good to have ½-size cases
 42 
Lines per order Distribution
 implication:
on the picking methods
 43 
Lines and Cube per order
Distribution
 implication:
on the picking methods
 44 
Items Popularity Distribution
 implication:
on storage zones, golden, silver,
bronze
 45 
Cube-Movement Distribution

implication: small items in drawers or bin shelling;
large items in block stacking, push-back rack
 46 
Popularity-Cube-Movement
Distribution
 implication:
on storage mode
 47 
Item-Order Completion
Distribution
 implication:
on mode of storage, e.g.,
warehouse within a warehouse
 48 
Demand Correlation Distribution
 implication:
on zoning of goods
 49 
Demand Variability Distribution
 implication:
variance of demand to set
safety stock
 50 
Item-Family Inventory
Distribution
 implication:
area assigned to different types
of storage
 51 
Handling Unit Inventory
Distribution
 implication:
different storage modes
according to the number of pallets on hand
 52 
Seasonality Distribution
 implication:
shifting human resources and
possibly space
 53 
Daily Activity Distribution
 implication:
shifting human resources and
possibly space
 54 
Activity Relationship
 implication:
on layout
 55 