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

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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

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Customers Retailers Warehouse/Dist Manufacturer Suppliers Suppliers’ Suppliers

Supply Chain: a Service Process

Source: www.dallasfed.org/research/swe/2005/swe0502b.html

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Customers Customer Reps References/Credit Title Search Loan Design Loan Execution

Another Service Process: Mortgage Application

C

C

C

CR

CRRC

TS

TS

TS

LD

LE

LE

LE

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

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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

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

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Control Chart

Basics

LCL

UCL

Time →

Out of Control →

= a statistic (individual value, average, range, std. dev.) for a subgroup of performance data

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Process Capability Basics

Cpk = min (USL – avg, avg – LSL) = 2.5/3

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USLLSL avg

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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?

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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

Supply Chain Process Execution Data

09/29/10 SPRC

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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

1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03

RFID

simplified structure

Enterprise structure

(distributed RFID read data) 1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03

1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03

1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03

2 001 12:002 001 12:012 001 12:022 002 12:022 003 12:022 001 12:03

1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03

3 001 12:003 001 12:013 001 12:023 002 12:023 003 12:023 001 12:03

1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03

4 001 12:004 001 12:014 001 12:024 002 12:024 003 12:024 001 12:03

5 001 12:005 001 12:015 001 12:025 002 12:025 003 12:025 001 12:03

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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

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

This neighborhood is a collection of four echosets (IDs from the same order in the same echoset) that have specified characteristics.

Order 4

Order 3

Order 2

Order 1

SIT Data and Timeliness

Timeliness is measured by sojourn time of an echoset or averaged over a neighborhood of echosets

Order 4

Order 3

Order 2

Order 1

SPC for Unloading Times

Sample

Sam

ple

Mean

127113998571574329151

800

600

400

200

0

__X=235.4

UCL=565.8

LCL=-95.0

Sample

Sam

ple

StD

ev

127113998571574329151

1000

750

500

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_S=169

UCL=434

LCL=0

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1

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6666

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22

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666

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22222222

1111

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Xbar-S Chart of McDUnloading (mins)

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.

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SIT Data and Sequence Correctness

With these data we can plot the subgroup average sequence error: SPC for Sequence Correctness!

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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

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SPC for Supply Chains:Difficulties

• Some Ideas:– Dependencies (multiple items in same truck)– Inherent time lags and censoring

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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.

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Network-based Covariance

• Entities traveling from 1-5 and 2-6 always share 3-4

1

4

5

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3

xi = s1-s5 time = wi + vi

yi = s2-s6 time = wi + bi

Cov(X, Y) = Var(W)

wi

xi

yi

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Network-based Covariance

• More realistic: entities traveling from 1-5 and 2-6 sometimes share 3-4

1

4

5

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xi = s1-s5 time = wi + vi

yi = s2-s6 time = ai + bi

Cov(X, Y) = Cov(A, W)

wi

xi

yi

ai

• 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

Efficiency of Common Link Covariance Estimators

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SPC for Supply Chains:Difficulties

• Some Ideas:– Inherent time lags and censoring

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Determining Sojourn Time at S for I

Time →

Items in I

sojourn

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Determining Sojourn Time at S for I

Time →

Items in I

sojourn

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Determining Sojourn Time at S for I

Time →

Items in I

’20%’ sojourn

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Censored Data Issue:Determining Sojourn Time at a Particular State Subset S for Item Subset I

Time →

Items in I

sojourn

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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

Questions?

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