SPC for Services: Timeliness and
Correctness Monitoring
Russell Barton
Department of Supply Chain and Information Systems
The Pennsylvania State University
Acknowledgments: John McCool. Jun Shu, Earnest Foster, Jeff Tew,
Lynn Truss, Smeal College Center for Supply Chain Research
National Science Foundation
Overview
• What do we mean by service quality?
• Process Execution Monitoring: “SPC for
Services”
• Optimization versus monitoring views
• Process execution monitoring: supply chain
timeliness and correctness
• The work to be done
2
Supply Chain: a Service Process
Customers
Retailers
Warehouse/Dist
Manufacturer
Suppliers
Suppliers’ Suppliers
Source: www.dallasfed.org/research/swe/2005/swe0502b.html
3
Another Service Process: Mortgage Application
LE
C
C
TS
CR
CR
RC
LD
TS
C
Customers
TS
Customer Reps
References/Credit
Title Search
LE
LE
Loan Design
Loan Execution
4
A (narrow) Service Process View
• Transactions moving through process steps:
• a mortgage application moving through credit
check, title search, loan design
• a business order moving through order assembly,
packing, loading, shipping, unloading, unpacking
• Two key characteristics:
• how much time in each step
• correctness of sequence of steps
Service Quality
• Timeliness of Service Processes
– Entity or transaction time in a particular location (state)
– Entity or transaction time between locations or states
• Correctness of Service Processes
– Entity processed through a correct sequence of steps
or locations (states)
– There may be more than one correct sequence
– The sequence often depends on the kind and/or ID of
the entity
6
Service Quality
• Timeliness and Correctness characterize
many types of service operations:
–
–
–
–
–
–
–
Processing a mortgage
Delivering a package
Cleaning an office building
Providing emergency room treatment
Providing an educational certificate or degree
Providing airline service
A supply chain operation
Process Execution
Monitoring: SPC for Services
• Idea: apply SPC and process capability
methods to timeliness and correctness
measures from service process execution data
• For semi-automated processes this is a special
kind of Workflow Monitoring
• For the remainder of this presentation, we will
focus specifically on supply chain processes,
but the approach can be applied to any
transaction processing system
Control
Chart
Basics
Out of Control →
UCL
LCL
Time →
= a statistic (individual value,
average, range, std. dev.) for a
subgroup of performance data
9
Process Capability Basics

LSL
avg

USL
Cpk = min (USL – avg, avg – LSL) = 2.5/3
3
10
SPC for Supply Chains: the Need
• Need for SPC/Capability
– Are your suppliers’ deliveries repeatable?
– What is their process capability relative to delivery
time windows?
– Can you detect changes (‘out of control’) in the
delivery timeliness before there is a crisis?
– What stages of the delivery process cause the
greatest variation in delivery time? How much
might delivery time variation be reduced?
– How do you tell on a daily or hourly basis which
parts of your supplier chains or delivery chains
need attention?
11
Contrasting Process Execution Monitoring
with the usual Supply Chain Management
Focus:
Optimization versus Monitoring
Objective
Tools
Minimize delivery time, cost
Optimization, Simulation
Promise a specific lead time
Process Capability
Select a vendor
Process Capability
Meet a specific lead time promise
Statistical Process Control
Identify and address SC anomalies
Statistical Process Control
12
Supply Chain Process
Execution Data
09/29/10
SPRC
Core of Supply Chain Execution Data:
the SIT Triple
•
•
•
•
Abstract view: SIT triple
S: state (RFID reader location)
I: ID for entity (Case ID)
T: time stamp
RFID
simplified structure
1
1
1
1
1
1
001
001
001
002
003
001
12:00
12:01
12:02
12:02
12:02
12:03
Enterprise structure
(distributed RFID read data)
1
1
1
1
1
1
001
001
1
2
001
1
2
002
1
2
003
1
2
001
1
2
1
2
12:00
12:01
001 12:00
12:02
001 12:01
12:02
1 12:02
3
001 12:00
001
12:02
1 12:02
3
001 12:01
002
12:03
1 12:02
4
001 12:00
1 12:02
3
001
003
1 12:02
4
001 12:01
1 12:03
3
002
001
5 001
12:00
1 12:02
4
001
12:02
1 003
3
5 001
12:01
1 12:03
4
002
12:02
1 001
3
5 001
12:02
1 003
4
12:02
5 002
12:02
1 001
4
12:03
5 003 12:02
5 001 12:03
14
Using SIT Data to Monitor
Timeliness and Correctness
• Sets of raw (s, i, t) data can be used to
characterize ‘timeliness’ and ‘correctness’
• Use ‘echoset’ and ‘neighborhood’ concepts
– To aggregate multiple reads
– To determine arrival to and departure from a
readable state
– Infer entrance to and departure from nonreadable
states
– To allow calculation and characterization at
different levels of aggregation
15
SIT Data
The plot shows RFID reads for 10 items at one
reader location, over time.
SIT Data and Timeliness
The boxes indicate echosets of RFID reads,
considered as an aggregate presence of a
transaction (or item) at a particular state over
a period of time
SIT Data and Timeliness
Order 4
Order 3
Order 2
Order 1
This neighborhood is a collection of four echosets
(IDs from the same order in the same echoset)
that have specified characteristics.
SIT Data and Timeliness
Order 4
Order 3
Order 2
Order 1
Timeliness is measured by sojourn time of an
echoset or averaged over a neighborhood of
echosets
SPC for Unloading Times
Xbar-S Chart of McDUnloading (mins)
800
1
1
Sample Mean
600
UCL=565.8
3
400
_
_
X=235.4
200
2
6
6 6666 66
2
6
6
66
0
1
15
29
43
57
22 2
6
66
71
Sample
6
666 66
26
2 22
85
99
2
6
6
2
66
662 2222222 22 66
113
6
LCL=-95.0
127
1
1000
Sample StDev
1
1
1
1
750
500
UCL=434
250
_
S=169
2
2 222
0
1
15
29
43
2
57
2
71
Sample
85
99
2
222 222
113
LCL=0
127
SIT Data and Sequence Correctness
• Correctness requires a three-dimensional
view of the SIT triple
• The next figure collapses multiple states onto
the vertical axis, which now capture both
state and id…
• For these items, the correct sequence is state
S1, then state S2, then state S4.
• Four groups have their data in the plot,
resulting in two correct sequences (S1, S2,
S4) and two incorrect sequences (S1, S4)
and (S1, S3, S2, S4) – can you see it?
SIT Data and Sequence Correctness
Recall SIT Data and Timeliness Plot
Order 4
Order 3
Order 2
Order 1
SIT Data and Sequence Correctness
SIT Data and Sequence Correctness
Monitoring Correctness
• Measuring path correctness involves comparing an
actual sequence of states to one or more prescribed
sequences.
• There are a number of algorithms for measuring such
matches, coming from fields such as language
processing and genome sequencing. One example is
Edit Distance.
• These algorithms generally rely on some form of
dynamic programming, and are computationally
tractable for a small number of sequence steps.
26
SIT Data and Sequence Correctness
With these data we can plot the subgroup average
sequence error: SPC for Sequence Correctness!
SPC for Supply Chains:
If Straightforward, Why is there Little Use?
• Difficulties:
– Availability of data
– Form of data
– Multivariate data (different shipment modes,
products, destinations)
– Dependencies (multiple items in same truck)
– Defining measures of timeliness and correctness
at multiple scales
– Inherent time lags and censoring
29
SPC for Supply Chains:
Difficulties
• Some Ideas:
– Dependencies (multiple items in same truck)
– Inherent time lags and censoring
30
Identifying Network-Based Dependencies
from Group Movements and other Causes
• If traveling common links is the major source
of covariance in times, efficient methods are
available to estimate covariances for different
items sharing all or part of their routes.
• Variances (and perhaps covariances) in
individual links paired with topology are
sufficient to estimate all path covariances.
31
Network-based Covariance
• Entities traveling from 1-5 and 2-6 always
share 3-4
1
5
xi
3
wi
4
yi
xi = s1-s5 time = wi + vi
yi = s2-s6 time = wi + bi
2
Cov(X, Y) = Var(W)
6
32
Network-based Covariance
• More realistic: entities traveling from 1-5 and
2-6 sometimes share 3-4
1
5
xi
3
wi
4
ai
yi
xi = s1-s5 time = wi + vi
2
yi = s2-s6 time = ai + bi
6
Cov(X, Y) = Cov(A, W)
33
Efficiency of Common Link
Covariance Estimators
• Let C1 be the usual covariance estimator based on xi and yi,
and C2 be common link estimator based on ai and bi.
Then Var(C1) = Var(C2) + Var(Q+R+S)
• Where Q, R, S are the usual estimators for Cov(V,A),
Cov(V,B) and Cov(W,B) respectively
SPC for Supply Chains:
Difficulties
• Some Ideas:
– Inherent time lags and censoring
35
Determining Sojourn Time at S for I
Items in I
sojourn
Time →
36
Determining Sojourn Time at S for I
Items in I
sojourn
Time →
37
Determining Sojourn Time at S for I
Items in I
’20%’ sojourn
Time →
38
Censored Data Issue:
Determining Sojourn Time
at a Particular State Subset S for Item Subset I
Items in I
sojourn
Time →
39
SPC for Supply Chains: Work to be Done
• Identification of technology gaps and
roadblocks to implementation (data
access, data cleaning, data structure)
• Research on modifications to SPC and
capability tools to apply to supply chain
data: dependence and censoring
• Develop best presentation formats
(dashboards) for capability and control
analyses to enable effective supply chain
management
40
Questions?
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