The document discusses the calculation of two service level metrics - Shipment Service Level (SSL) and Release Service Level (RSL) - used at Sundstrand to measure inventory management performance. SSL measures on-time shipments to customers, while RSL measures the performance of the inventory planning system by excluding factors outside its control that could impact SSL. RSL success is defined as shipping or scheduling to ship by the target date, while SSL also requires actual shipment by the customer's requested date. The document provides examples of how orders are classified for each metric based on factors like short-cycled orders, supplier delays, and failures in the pick/pack/ship process.
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Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. The Calculation of Service Levels
1
The efficacyof the inventorymanagementsystemismeasuredby (1) the inventoryturnratioand (2) the
service leveltothe customers. AtSundstrandthere were twocalculationsforservice level–SSL and
RSL.
SSL
Shipmentservice level (SSL) measuresthe on-time shipmentof itemstocustomers. It’sthe service level
the honchos want to knowabout. It iscalculatedeachmonthon the populationof line items thatwere
shipped duringthe month. A successisachieved if aline itemshipsonorbefore the customer’s
requested shipdate. Otherwise,the lineitemisconsideredaloss. SSL isusuallyexpressesasa
percentage - E.g.95.2%. Typically,there isamanagementdirectedtargetforthis metric. At
Sundstrand,thistargetvariedovertime. Whentimeswere toughthe targetusuallywasloweredto
save moneyoninventory.
The “how-to”for the calculationof SSL wasspecifiedandstandardizedbythe airlines underthe
authorityof the Airline TransportAssociation. Sundstrand’scalculationconformedtothe ATA
specification.
RSL
There are forces“outside”of the inventoryplanningsystemthat canimpactSSL. Mr. D wanted to
eliminatethose impactsandmeasure onlythe performance of the inventoryplanningsystem. So,he
devisedananalogous measure called“release service level”(RSL).
The RSL measuresthe performance of the inventoryplanningsystem. Itisalso calculatedmonthly,but
not onthe same population of orders asthe SSL. Rather,it iscalculatedonthe new orders (line items)
that were received duringthe month. If a line itemis shipped orisscheduled toshiponor before the
target shipdate thenthe line itemiscountedassuccess. Otherwise,itiscountedasa loss. RSL is also
expressesasapercentage. Itisnoteworthythatan orderdoesnot actuallyhave toshipto be an RSL
success.It justhad to be promisedtoshiponor before the customerrequestshipdate.
There are several thingsthatcause the RSL and the SSL to differ.
First,there isdelinquencyfromthe suppliers. AtSundstrandthe procurementleadtimeswere “sent
down”to the COOPsystemfromthe MRP system. The COOP systemschedulednew replenishment
ordersat full leadtime. There were almostnoexceptionstothisrule. Regrettably, sometimesthe
suppliersdidnotdeliveronschedule, soCOOPranshortof parts and shipmentswerenotmade ontime.
Thiswouldresultin an SSL loss,butnotnecessarily aRSL loss. The penultimate example of thiswould
be the supplierscompletelystoppedthe deliveryof parts. Thiswouldresultinthe SSLgoingto zero,but
the RSL could continue to be at target levels. Actually,arobustplanningsystemwoulddojustthat.
Second,sometimesthe airlines“shortcycled”theirorders. Inaccordance withthe ATA specification,
the critical date used in calculatingSSListhe customer’s “requestedshipdate”. Itwas notuncommon
2. The Calculation of Service Levels
2
for an airline torequestashipmentdate thatwaspriorto Sundstrandactuallyreceivingthe airline
order. Those orderswere always SSLlosses. ForRSL calculations,shortcycleshadtheirrequestedship
date adjustto equal the bookdate pluscatalog leadtime. Therefore,some of these shortcyclescould
be RSL successes. Inthe 1980s and 90s, whenpaperorders andsnail mail were king,shortcycledorders
were more common.
Thirdly,there canbe failure in the pick/pack/shipprocess. Anitemmayhave the pick requestsentto
the stockroomon time,butforsome reasonthe partsdon’tget packagedandshippedon time. That
wouldbe a SSL loss. If the pickrequestwas initiatedpriortothe customerrequestedshipdate thenit
wouldbe a RSL success.
Every monthMr. D would re-optimize inventory performance. The individual catalogitemswouldhave
theirtargetservice levelsresetsothatthe dollarsof safetystockrequiredtomeetthe aggregate service
level wasminimized. Itisimportanttonote that itwas the target RSL that wasbeingchanged. Stated
differently, RSLismetricthat mustbe usedtomeasure the performance of the inventoryplanning
system.
In hisquartercentury of experience Mr.D foundthe SSL to be abouttwo percentbelow the RSL. So,if
the managementgoal wasto achieve anSSL of 94%, Mr. D wouldsetthe aggregate RSLat 96%. Mr. D
has writtenapaperon howto optimize service levels.
Here are some examples of SSLandRSL calculations.
Customer
C.O.
Number
Line
Number ItemNo Total Qty
Control
Ship Priority
Catalog
Lead
Time
Cus
Rqstd
Ship Date
Receipt
Date Book Date
ZZZ DQT-123 1 A123 10 7 1-Jan-13 1-Jan-13
SYS
Rqstd
Ship Date Dtl Qty
Order
Status
First S/P
Date
Last S/P
Date Pick Date Ship Date
RSL
Target
Date
RSL Done
Date Note
8-Jan-13 10 S 8-Jan-13 8-Jan-13 1-Jan-13 2-Jan-13 8-Jan-13 1-Jan-13 Standard order
Table 1
Table 1 showsanorder froma customerfor 10 piecesof itemA123. ItemA123 is a catalog itemand
customerscan expectshipmentof the item7 daysafterreceiptof order. This customerdidnot specify
any control shipmentrestrictions, andtheyalsodidnotspecifyanyrequestedshipdate. Thiswasan
electronicallyreceivedorder,soitwasreceived, andcompletedthe bookingprocess,on1-Jan-13.
Because the customerdidnotspecifyarequestedshipdate the systemassignedarequested shipdate -
as the “book date plusthe catalog leadtime”. Since the DTL Qty matchesthe Total quantityall of the
quantityorderedwasavailableatthe same ship/promisedate –inthiscase 8-Jan-13. These partswere
available instockandinfact pickedon1-Jan, butper the ATA spec the ship/promise date wasreported
back to the customeras bookdate + catalog leadtime. The partsshippedtothe customeron 2-Jan.
3. The Calculation of Service Levels
3
Since the shipdate isearlierthanthe systemrequestdate thisisa SSL success,andalsoa RSL success.
Thisorder wouldbe includedinthe SSLanalysisforJanuary,since itshippedinJanuary. Likewise,it
wouldbe inthe January RSL analysis,because it“booked”inJanuary.
Thiscustomerisa Sundstrandrepaircenter,andnota “real” airline. Therefore,itisnotcountedinthe
SSL analysisdone bythe OrderAdministrationgroup. Mr.D ran a separate SSL analysisjustorthe
Sundstrandrepaircenters. The service level toSundstrandrepaircenterswas basically identical tothe
service leveltocustomers. Thisshouldnotbe surprising,asall orderswere processedthroughthe same
processlogicinthe COOP system.
Consideranotherexample.
Customer
C.O.
Number
Line
Number ItemNo Total Qty
Control
Ship Priority
Catalog
Lead
Time
Cus Rqstd
Ship Date
Receipt
Date Book Date
DLA Df-09978 2 D456 1 C 7 10-Jan-13 5-Jan-13 5-Jan-13
SYS
Rqstd
Ship Date Dtl Qty
Order
Status
First S/P
Date
Last S/P
Date Pick Date Ship Date
RSL Target
Date
RSL Done
Date Note
10-Jan-13 1 S 19-Jan-13 19-Jan-13 12-Jan-13 12-Jan-13 12-Jan-13 12-Jan-13 Ok RSL, miss SSL
Table 2
Thisis a critical priorityorderfora catalogitem. The orderwas bookedon5-Jan andthe customer
requestedshipmenton10-Jan. The COOPsystemdidan ATP analysis andgave a firstS/P date of 19-Jan.
Since itwas a critical orderthe inventoryplannertookactionandthe part was stockedearlyon12-Jan,
whichresultedinthe ordershippingon12-Jan. Still,the 12-janshipdate was a SSL loss.
From the RSL perspective the initialS/Pdate wasa RSL loss. However,because of the expediting done
by the plannerthe orderendedupas an RSL success. Note thatthe date for an RSL successisnot
necessarilythe customerrequestedshipdate. Inthiscase it is “book date + catalogleadtime”whichis
12-Jan-13. Why? Because Sundstrandadvertisedtotheircustomersthatpartswouldshipone catalog
leadtime afterreceiptof order. RSL measuresthe successof the planningsystemtothatstandard.
Since the line itempickedon12-Jan,thisisan RSL success.
4. The Calculation of Service Levels
4
Here is one lastexample.
Customer
C.O.
Number
Line
Number ItemNo Total Qty
Control
Ship Priority
Catalog
Lead
Time
Cus
Rqstd
Ship Date
Receipt
Date Book Date
AAT R889 2 A123 3 Y 7 22-Jan-13 15-Dec-12 16-Dec-12
SYS
Rqstd
Ship Date Dtl Qty
Order
Status
First S/P
Date
Last S/P
Date Pick Date Ship Date
RSL
Target
Date
RSL Done
Date Note
22-Jan-13 3 S 22-Jan-13 22-Jan-13 15-Jan-13 19-Jan-13 22-Jan-13 15-Jan-13 Control Ship, shipped OK
Table 3
Thisis a control shiporderfrom a customer. The order wasbookedon16-Dec-12 witha requestedship
date of 22-Jan-13. The parts to coverthisorderwere instock,but, because of the control ship,the parts
were notpickeduntil 15-Jan,andwere thenshippedon19-Jan. The S/Pdatesacknowledgedtothe
customerwere hisrequestedshipdate,since partswere availablebeforethen. Hadthe ATPprocess
committedtoa date laterthan22-Jan-13 thenthe S/Pdate wouldhave beenthat laterdate. Thisorder
isa successforboth the RSL and SSL. But, since the orderwasbookedinDecember, the RSLsuccesswas
countedinthe Decembermetrics. The shipmentoccurredinJanuary,sothe orderwas countedinthe
JanuarySSL metrics.
Mr. D couldgive more examples,butthe nitty-grittyof the Sundstrandpromise date logicisnotwhat
thispaperis about.
The salientpointtobe made is that there are two service level metrics:RSLandSSL. The typical
inventoryplanninglogicattemptstostatisticallyforecastvariationsindemand andsetinventorylevels
inresponse to those variations. The planningsystem doesnotattempttoforecastsupplierdelinquency,
and all of the otherfactorsthat can make RSL differfromSSL. Therefore,RSListhe metricof interestto
the inventorymanager. Historywill show afairly consistentdifference betweenRSLandSSL.
At Sundstrand,the SSLmetricwas the domainof the OrderAdministrationdepartment,althoughMr.D
was the authorof the program theyusedtodo the analysis. Theiranalysisonlyconsideredordersfrom
“outside”customerssuchasairlinesandthe occasional thirdpartyrepairshops. The SSL also included
shipmentsfornon-catalogitems.
Mr. D tracked the SSL tothe Sundstrandrepaircenters,and alsothe RSL to all customers,bothoutside
and inside –repaircentersandairlines. Onlycatalogitemswere includedinthe Mr.D’s analysis. Thisis
because non-catalogitemsprovide nouseful insighttothe functioningof the planningsystem.
Well there itis:one more tasty entrée inMr. D’s smorgasbordof ideas. Take whatyou want,andleave
the rest.
Contact Mr. D at MisterD@windstream.net if youhave anyquestions.