REALTIME PREDICTIVE AND PRESCRIPTIVE ANALYTICS WITH REAL-TIME DATA AND SIMULATION. Hosni Adra. Simulation and Optimization Manager/Partner

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1 Prceedings f the 2016 Winter Simulatin Cnference H. Adra REALTIME PREDICTIVE AND PRESCRIPTIVE ANALYTICS WITH REAL-TIME DATA AND SIMULATION Hsni Adra Simulatin and Optimizatin Manager/Partner CreateASft, Inc th Street, Suite 105 Aurra, IL 60504, USA ABSTRACT The past few years have experienced tremendus grwth in the use f simulatin t imprve the wrk place and the efficiency f every peratin. Althugh prcessr speeds have increased at a very fast pace, simulatin, due t its extensive cmputatinal and visualizatin requirements, have cnsistently challenged and used the prcessrs t their full pwer. Mrever, with the evlving 64bit simulatin engine technlgy, simulatin mdels have increased in size and cmplexity with extensive memry requirements, ever expanded data input sets, alng with increased cnnectivity requirements. In additin, real-time data is becming mre accessible than ever, and it has greatly cntributed t simulatin mdels accuracy, validity and usability. This paper discusses the reusability and extensibility f simulatin mdels and their rles in predictive and prescriptive analytics using real time data cnnectivity as used in SimTrack real time predictive analytics and schedule adherence. 1 INTRODUCTION Large simulatin mdels, spanning full warehuses, manufacturing plants, hspital campuses, and ther types f envirnments are cmplex, cmputatinally intensive, and require mre time t run using traditinal single threaded simulatin envirnments. Take fr example a warehusing implementatin that invlves multiple AS/RS systems, standard racking, AGVs fr material handling, autmatin equipment, high speed lines, and, mst f all, human wrkers cnsistently interacting with all aspects f the warehuse. T drive such a system, data must be available frm the WMS (Warehuse management system), WCS, PLCs, RFID and ther data pints tracked thrugh either WMS, SAP, barcde, RFID, RTLS, r manual entry. Fr ffline runs, waiting fr the mdel t cmplete the run is nt as critical r demanding. On the ther hand, when the mdel is prviding real time predictive analytics, mdel run turn-arund time, including multiple scenaris and Mnte Carl runs, becme critical. Whether the implementatin is fr schedule adherence, predictive bttleneck detectin, r ther efficiency and imprvement alerts, tw key factrs impact the verall slutin; simulatin speed, and mdel validity and accuracy. Simulatin speeds can be imprved by better utilizing the underlying hardware system. Whether smaller, independent and interacting mdels are used r a highly distributed single mdel implementatin is used key challenges arise in mdel synchrnizatin and real-time data availability. As discussed later, there are many methds that can be implemented t successfully distribute the mdel and achieve extensive imprvement in simulatin run speed withut disturbing mdel validity.

2 Mdel accuracy, n the ther hand, can be even mre challenging, especially when real time data is used. In the case f static envirnments, the mdeler is aware f the different prcess steps and mvements that parts r resurces are taking. Mdel cnstraints can be impsed using the knwn data and current cnstraints. In the case f real-time data and real-time predictive envirnments, mdels need t be designed in a way that allws them t grw with the system, enable real-time cnstraint changes, and in sme case, expand themselves in rder t maintain the crrect relatinship with live data. In ther wrds, mdels need t run within an intelligent envirnment that allws them t mdify their cnstraints r prcess steps based n data feedback frm external data surces and withut human interventin. This is a key requirement that enables systems t be used as turn-key, hands-ff systems that are cnstantly evlving within their envirnment while maintaining a high degree f validity and accuracy. 2 CHALLENGES The presented technlgy, althugh evlving, has successfully been implemented and has vercme many challenges acrss multiple industries. When simulatin mdels interact, they need t be /intelligently synchrnized in rder t prtect the integrity f the results and validity f the mdel. Whether the implementatin spans a single system using multithreading methds, r multiple systems using inter prcessrs synchrnizatin, the synchrnizatin is ne key item that must be cntrlled. In additin t the synchrnizatin challenge, simulatins interacting with external, real-time data systems, must maintain the integrity f the integratin with utside systems. In ther wrds, real-time data needs t be clearly mapped t the mdel in a way that prevents ambiguity while prviding an efficient redundancy level. Whether the data is being retrieved frm external data stres (Databases, SAP, WMS, ERP, EMR, ) r received asynchrnusly thrugh PLCs r RFID data, the simulatin mdel(s) autmatically update and the utcmes f the changing cnditins are used t identify bttleneck and areas fr imprvement in real-time. The mst challenging aspect f intelligent mdels is their ability t cnstantly evlve based n the realtime envirnment withut disturbing the validity f the mdel. Such mdels are develped in a way t adapt t incming data and add r remve cnstraints based n the actual envirnment. Intelligent mdels cnsistently learn frm the current envirnment, readjust n-the-fly and cnsistently perfrm validatin, analysis, and ptimizatin in rder t prvide a hands free envirnment. Examples f real-time mdel interactin and learning intelligent mdels are presented. 2.1 Mdel Building There are three methds in which t classify the mdels as intercnnected netwrk f simulatin mdels; mdel(s) utput(s) feeding ne r many mdel inputs, tightly synchrnized mdels in a multithreading envirnment, r distributed prcessing with synchrnized mdels acrss the verall system. When real time data cnnectivity are used, the methdlgy is nt as critical as the mdeling apprach. When real time data and cntrls are used (such as RFID, GPS, and ther RTLS time systems), simulatin mdels shuld be built and defined t allw fr the external tracking system t autmatically update and mdify the mdel behavir and cnstraints. In ther wrds, if a frk truck item has been identified t be transitining t a new pallet lcatin the mdel shuld be smart enugh t allw the mvement and autmatically update the mdel cnstraints with the new pallet lcatin. The same set f mdels will be used in multiple ways ; Real time visibility shwing the lcatin f each item, prgress and analytics fr every entity within the system.

3 Predictive mde where the mdel cnstantly runs in the backgrund, cnsuming histrical data, prcessing real time cnstraints, and generating predictive analytics while prviding alerts and ntificatins as t the impact n the future state f the peratin. Prescriptive mde where the mdel ptimizes the future state with prcessing changes required t imprve the efficiency f the peratins predicted future-state. Whether rescheduling jbs, labr, deliveries, r re-sequencing the line with updated WCS r PLC lgic, r mdifying prcess flw and prcessing cnstraints a prescriptive mde prvides fr an ptimized future state with minimal human interactin and n ffline analysis required. 2.2 An Intelligent mdel with Real time cnnectivity Whether the mdels are cnnected t an HL7 data feed, WMS, WCS, ERP, r RTLS systems, each ne f them needs t be able t mdify itself in rder t supprt the data feed. Self-Mdifying mdels are able t detect changes in the external envirnment and cnstantly evlve in rder t supprt the new cnstraints. An example f an evlving mdel wuld be adding new rms t an ED department, changing the pick path in a warehuse, adding a new rack, inserting a new machine int the flw, r generating a new assembly type. Dynamic Self-Evlving simulatin mdels are built using Simcad Prcess Simulatr, a dynamic envirnment where mdels autmatically update and mdify their cnstraints and flw during the simulatin run. In additin, since Simcad Pr allws mdels t aut-recmpute the distributins based n histrical data, it is used as the underlying simulatin engine t drive the Mnte Carl analysis and is incrprated in bth predictive and prescriptive envirnments. Extending the mdels used in ffline analysis, SMARTLS and SimTrack prvide the cnnectivity t real time tracking systems, r multiple data systems as needed. In additin, SimTrack includes a PLC cnnectivity mdule that enables the mdel t cnnect t real time PLC infrmatin. The PLC cnnectr can be setup as a dual channel cnnectr allwing the SimTrack envirnment t read and write t the PLCs. 3 SYSTEM GENERATED RESULTS As the system evlves and mdels interact, analytics and perfrmance tracking metrics are dynamically generated. The generated analytics and tracking metrics are then used fr tracking and visibility, predictive and prescriptive analytics. 3.1 Real-Time Visibility This mdel view prvides real-time visibility t the tracked envirnment. Each entity (equipment, prduct, peple, resurces, thers) are displayed in a visual representatin and are equipped with a hver feature r reprting dashbard that enables a user t check the status and prperties f each entity within the system. Mdel metrics are cmputed in real-time and displayed t the user via web enabled dashbards based n current mdel cnstraints. System tracking, visibility, and metrics are published t a web interface in rder t prvide users access t the interface and display the infrmatin n desktp and mbile devices. System visibility representatin is presented in either 2D r 3D space depending n the underlying hardware infrastructure and visualizatin requirements.

4 3.2 Predictive Analytics Adra As described in this dcument, the mdel has the ability t learn frm current envirnment and evlve itself in rder t better imprve its predictive values. The mdel starts ut initially based n the initial cnstraints defined within its settings. This is cnsidered the system startup, and ccurs nce per mdel per installatin. This initial runs serve 2 main purpses Generate the initial current mdel state Cllecting external cnstraints that are influencing the mdel prgress, including dwntime, cycle times, arrival patterns and thers. As the SimTrack predictive mdel evlves, the fllwing peratins ccur General distributins f cycle times, arrival patterns, dwntime, and ther factrs are cmputed at each run. Histrical distributins are cmputed based n change factrs and patterns that relates t the validity f the data. As an example, far histry are weighed less then near histry, and current daily events have mre impact than either f the histrical data sets. Knwn datasets cntaining nearfuture schedule r shipping requirements als impact the generated analytics. Multiple Mnte Carl runs are perfrmed in rder t avid the variability created frm the distributin and t establish a valid and accurate representatin f the future f the peratin. Data generated frm the predictive mdel is displayed directly t web enabled dashbards prviding great insight n the current and future status f the peratin. The predictive envirnment generated is highly accurate as it is derived frm real-time cnstraints driven by a distributin f current, histrical and knwn future data sets. Real-Time schedule adherence is generated based n the current status and predicted future f each peratin. Schedule deviatin, slippage, and n-time deliveries are dynamically cmputed and displayed t the user. Events and alerts are generated when unexpected events ccur r schedule slippage is detected. The predictive mdel is able t determine the amunt f generated delays, their cause, and ptential impact n ther sectins f the peratin. 3.3 Prescriptive Analytics In the prescriptive mdel mde, real-time ptimizatin and validatin is perfrmed in the backgrund. Fr each ptential ptimizatin methd, and similar t the predictive envirnment, a set f Mnte Carl simulatin is run in rder t determine the viability f the slutin. The number f required runs is determined by the system and is driven by mdel cnvergence factrs as they relate t the ptimized values. Examples f real time ptimizatin include identifying the best pick rute and pick schedule fr a warehuse, mdifying put-away and replenishment sequences, mdifying schedules, man-pwer requirements r maintenance schedules in a manufacturing

5 envirnment, r re-scheduling OR patients based n OR lad levels, delays and current state impact. During the real-time ptimizatin, a number f ptential imprvements culd be determined. Each viable slutin requires a number f changes t the real system in rder fr the predicted results t be valid. The system can react t the ptimizatin suggestin in different ways depending n its cnfiguratin. Accept the requested change that fit within the allwed change cnstraints, prpagate the change t external system, and prceed with the tracking. Present the user with a number f ptins and allw the decisin t be made by the manager in charge. When a slutin is accepted, the system can either push the change t external systems, r mnitr the envirnment fr change. In this case, it is the manager s respnsibility t effect the change n the external system Anther key benefit f prescriptive analytics is its impact n schedule adherence. As different ptimizatin ptins are selected, an insight int the future f n-time delivery is presented alng with ptential impact f change frm the current state. In ther wrds, each ptimizatin methd impacts the n-time delivery and efficiency f the envirnment. Thse changes, delays r imprvements, are psted n live dashbards with varying degree f detail depending n the dashbard audience. 4 MULTIPLE COLLABORATION MODELS Fr the system t be effective, the implementatin shuld be divided int multiple intercnnected mdels wrking tgether in rder t prvide the final results. The mdels can run in 3 different mdes; Synchrnized mde, where multiple mdels are synchrnized in time, cnstraints and behavir in rder t prvide a faster executin state in a mre cntrlled envirnment. There is n limit n the number f synchrnized mdels that are run, and multiple hardware platfrms may be used in rder t speed up the executin. Sequential mde, where multiple mdels build n each ther results. When the first mdel cmpletes, it creates the input data set fr the next mdel t run. Upn mdel start, the secnd mdel relads its cnstraints, aut cmputes its distributin and prceeds with the executin. Nte that in a sequential mde, a mdel run may cnsists f multiple Mnte Carl runs wrking tgether t generate the input t the next mdel. The third mde is a hybrid implementatin f sequential and synchrnized mdes. Based n the system requirements and result definitin, the mdel executin can switch frm synchrnized t sequential multiple times during the executin cycle. In all mdes, the underlying scheduler in SimTrack takes care f all required synchrnizatin and sequential executin as defined in the system prperties. The burden f synchrnizing the mdel is nw a standard functinality f the cntrlling system hence reducing the requirement n the mdel develper t implement mre stringent synchrnizatin methds. The resulting envirnment is scalable in executin speed, mdel cmplexity and verall system accuracy. 5 SUMMARY The presented envirnment is designed t prvide a cmplete predictive and prescriptive envirnment built n dynamic, real-time changing, simulatin mdels. The presented SimTrack is designed t take simulatin

6 envirnment t the next level and make it an integral part f any rganizatin tlset. The generated data set, whether in real-time visibility, predictive r prescriptive envirnment, enables rganizatins t utilize the pwer f simulatin in rder t maximize the peratinal effectiveness and imprve the n time delivery and scheduling. Existing systems implemented in healthcare, manufacturing, and warehusing have prven t be indispensable and have generated an ROI that far exceeds the initial implementatin cst within the first year f the implementatin. AUTHOR BIOGRAPHIES HOSNI ADRA is the c-funder f CreateASft, Inc. He has been invlved in Prcess Imprvement and Simulatin fr the past 25 years. Hsni has applied his prcess imprvement expertise t multiple industries including healthcare t increase efficiency and reduce perating risk. As the hlder f several patents in the fields f dynamic simulatin and tracking, Hsni has been a sught after expert in these fields and has presented multiple papers n prcess imprvement using simulatin and implementing lean cncepts. With his dedicatin t the use f technlgy t imprve efficiency and utput, he has psitined CreateASft as a leader in the prcess imprvement industry. His address is hadra@createasft.cm. Simcad Pr, SimTrack, CreateASft, and Dynamic Simulatin are registered trademarks f CreateASft, Inc.

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