Presentation on theme: "SPC for Services: Timeliness and Correctness Monitoring Russell Barton Department of Supply Chain and Information Systems The Pennsylvania State University."— Presentation transcript:
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
2 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
3 Customers Retailers Warehouse/Dist Manufacturer Suppliers Suppliers Suppliers Supply Chain: a Service Process Source:
4 Customers Customer Reps References/Credit Title Search Loan Design Loan Execution Another Service Process: Mortgage Application C C C CR RC TS LD 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
6 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
9 Control Chart Basics LCL UCL Time Out of Control = a statistic (individual value, average, range, std. dev.) for a subgroup of performance data
10 Process Capability Basics Cpk = min (USL – avg, avg – LSL) = 2.5/3 3 USLLSLavg
11 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?
12 Contrasting Process Execution Monitoring with the usual Supply Chain Management Focus: Optimization versus Monitoring ObjectiveTools Minimize delivery time, costOptimization, Simulation Promise a specific lead timeProcess Capability Select a vendorProcess Capability Meet a specific lead time promiseStatistical Process Control Identify and address SC anomaliesStatistical Process Control
Supply Chain Process Execution Data 09/29/10SPRC
14 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 : : : : : :03 RFID simplified structure Enterprise structure (distributed RFID read data) : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :03
15 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
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
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!
29 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
30 SPC for Supply Chains: Difficulties Some Ideas: –Dependencies (multiple items in same truck) –Inherent time lags and censoring
31 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.
32 Network-based Covariance Entities traveling from 1-5 and 2-6 always share x i = s1-s5 time = w i + v i y i = s2-s6 time = w i + b i Cov(X, Y) = Var(W) wiwi xixi yiyi
33 Network-based Covariance More realistic: entities traveling from 1-5 and 2-6 sometimes share x i = s1-s5 time = w i + v i y i = s2-s6 time = a i + b i Cov(X, Y) = Cov(A, W) wiwi xixi yiyi aiai
Let C 1 be the usual covariance estimator based on x i and y i, and C 2 be common link estimator based on a i and b i. Then Var( C 1 ) = Var( C 2 ) + 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
35 SPC for Supply Chains: Difficulties Some Ideas: –Inherent time lags and censoring
36 Determining Sojourn Time at S for I Time Items in I sojourn
37 Determining Sojourn Time at S for I Time Items in I sojourn
38 Determining Sojourn Time at S for I Time Items in I 20% sojourn
39 Censored Data Issue: Determining Sojourn Time at a Particular State Subset S for Item Subset I Time Items in I sojourn
40 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