This paper presents a proof of concept for a Military Logistics NetworkPlanning System (MLNPS) to be used during mission planning to quicklyidentify a robust logistical footprint that canadequately sustain units deployed in an expeditionary environment. The logistical network is modeled using an efﬁcient formof goal-seeking deterministic discrete eventsimulation to process supply requisitionsthrough the logistical network. The queuinginformation obtained from the simulationinforms capacity adjustments to the net-work to maximize efﬁciency. This process of simulation and network tuning continues interactively until an adequate and robustlogistical footprint is found. During theplanning stages, the MLNPS can be used toidentify and mitigate logistical problemsinstead of waiting to react to backlogs whenthe military’s operations would have al-ready been affected. Designed to run as an app on the Army’s enterprise resource planning (ERP) system (Global CombatSupport System-Army), the MLNPS canalso be used during operations to informcommanders of expected operational im-pacts on logistics. Contingency operationscenarios are used to demonstrate theMLNPS’ capabilities. INTRODUCTION The US military supply network is the largest in the world (Parlier, 2011). The network consists of 24 wholesale supply locations and ;33,000 retail supply loca- tions across ;86 countries. It uses ;10 dif- ferent modes of transportation, receives;50,000 daily requisitions, and manages ;2.4 million lines of inventory that total ;$109 billion in inventory (Faris, 2014). Managing a network this large in an efﬁ- cient and cost-effective manner requires enlightened management, the applicationof advanced analytics, and comprehensiveplanning. Historically, logistical planningfor contingency operations has not em-braced the size and complexity associatedwith the US military logistical network. With the release of Defense Reform Initiative Directive #54 in 2000 (DeputySecretary of Defense, 2000), logistic trans-formation plans focused on three themes: 1)the need for automated decision-supporttools, 2) the need for an end-to-end per-spective, and 3) the need to leverageemerging technology. Deputy Secretary ofDefense John J. Hamre stated that logistics efforts need to “leverage emerging tech- nology to increase the visibility, accuracy,and speed of logistics operations withoutcompromising our effectiveness” (Hodge,2001, p. 2). The Joint Vision 2020 echoedsimilar themes; Chairman Joint Chiefs ofStaff General Shelton mentioned the needfor an “end-to-end set of information ca- pabilities .and enhanced decision- support tools that will improve analysis, planning, and anticipation of warﬁghterrequirements” (Ofﬁce of Chairman of theJoint Chiefs of Staff, 2000, pp. 9, 24). Logis-tical challenges encountered in 2003 duringOperation Iraqi Freedom (OIF) reinforcedthe need for logistical improvements. A 2005 study by the RAND Corporation on the logistical operations of OIF found sig-niﬁcant logistical deﬁciencies due in part toa lack of theater distribution planning anddecision-support tools. Motivated by sig-niﬁcant readiness degradation to the USArmy’s 3rd Infantry Division during the in-vasion of Iraq, the authors recommended that “planning tools and organizational structures need to better support expeditionary opera-tions” with “effective automation to rapidlydetermine capability requirements” (Peltzet al., 2005). Our model seeks to answer that call to action. In July 2005, the United States Army began an “IT Solution” project designed to replace more than 200 standalone and re- dundant legacy logistical systems with onefully integrated modern logistical system.This new database system is comprised ofthree different enterprise resource plan-ning (ERP) tools and is powered by thecommercial-off-the-shelf enterprise soft-ware system, SAP /C210(www.sap.com). Toge- ther, these three confederated ERPs makeup a web-based end-to-end system that al-lows the Army to see and share supplychain information in real time. One of theseis the Army’s tactical ERP system, known asthe Global Combat Support System-Army(GCSS-Army, http://gcss.army.mil/) (Parlier,2016). To our knowledge, no current logistical planning or decision-support tool harnesses t h ed a t as t o r e di nG C S S - A r m y .W i t hG C S S -Army, it is possible to create an automatedplanning tool to rapidly determine end-to-endA Military Logistics Network PlanningSystem Matthew B. Rogers, Brandon M. McConnell, Thom J. Hodgson,Michael G. Kay,Russell E. King,Greg Parlier, andKristin Thoney-Barletta North Carolina State University APPLICATION AREAS: Resources–Logistics,Joint Warfare,Expeditionary Warfare OR METHODS: Network Methods, Simulation, Forecasting Military Operations Research, V23 N4 2018, doi 10.5711/1082598323405 Page 5Rogers, M.B., McConnell, B.M., Hodgson, T.J., Kay, M.G., King, R.E., Parlier, G., & Thoney-Barletta, K. 2018. A Military Logistics Network Planning System, Military Operations Research, Vol 23, No 4, 5–24. logistical capabilities requirements for the US Army. This article presents a prototype for a tool that uses advanced analytics and mission-based forecasting (Parlier, 2011), an emerging meth-odology for predicting military repair part de-mand, that helps predict choke points in anexisting logistical network and provides valu-able information about how to eliminate thosechoke points. The proposed tool uses a rapid,repetitive, goal-seeking simulation to model the routing of all requested supplies from receipt of the requisition to delivery of the supplies tothe customer. It routes the supplies along theavailable transportation modes subject to theavailable transportation assets, while satisfyingthe customer by minimizing the maximumlateness of all requisitions. When simulating themovement of all requisitions across the entire network, queuing occurs at logistic nodes. The identiﬁcation of queuing along speciﬁc trans-portation routes signiﬁes potential choke pointsthat may arise due to daily logistical requ-irements or, most likely, by the increased de-mands from global deployments and othermission sets. Once potential choke points areidentiﬁed, the decision-support tool allows the decision maker to shift or add transportation assets, or other logistical capabilities, to reduceor even eliminate these choke points. The toolcan then run with the revised logistical net-work and once again determine potentialchoke points. This process can be repeated inreal time until the logistical network can sus-tain all operational missions or the decision maker determines that an operational mission or planned mission must be adjusted. With the advanced analytics running the model and mission-based forecasting to helpimprove the model’s predictive capability, thisdecision-support tool can be used in severalways. The model can be run on a daily basis tohelp keep the Army’s logistical network run- ning in an efﬁcient and cost-wise manner. It can be used as needed to determine the impact ofpotential disruptions such as weather and en-emy actions on the network. The tool can alsopredict the logistical impacts of operationalplans during contingency planning. This allowsArmy senior leaders and planners to have lo-gistical contingency plans “on the shelf.” Lastly,the tool allows senior leaders and planners to have a proof of concept tool during crisis actionplanning. The tool allows them to plan the lo- gistics of a mission as quickly as they can con- duct the operational planning to see if theoperational plan is logistically feasible. Thespeed of the tool actually permits them to planthe logistics faster than operational planningand present logistical impacts of different sizemissions during mission analysis. Although a few speciﬁc functions of the proposed decision-support tool have been dis- cussed, the possibilities go far beyond just thosefew. The tool can be adapted to meet other lo-gistical requirements that can further improveArmy supply chain management to includemanning requirements over time at nationaldistribution points (shown later in this paper).These adaptations can readily include reverse logistics as well as other classes of supply to include the fuel network. Of particular interestis rapidly assessing the impact of disruptionsand the required capacities of the logisticalnetwork to support expeditionary operations asthese assessments are often required to be donein a time-constrained environment and informsenior leader decisions related to US national security strategic options. This paper presents a novel approach to estimating logistical plan performance, identi-fying a feasible and acceptable capacity plan,and performing “what if” analysis to assessimpacts of disruptions to the distribution net-work or other potential scenarios. Model vali-dation uses logistical data from the 2003 invasion of Iraq to apply this method to the OIF planning scenario to demonstrate model func-tionality. Then, using a notional operationalscenario set in Africa, we demonstrate how touse the tool to plan a logistical network andassess operational impacts of possible disrup-tions. The intent is to equip military logisticsplanners or analysts with a tool that can support rapid logistics analysis and is capable of using GCSS-Army data. LITERATURE REVIEW The basis of the advanced analytics needed to solve the problem at hand is akin to what is Page 6 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM needed to optimize the ﬂow of jobs with due dates through a factory. The Army logisticalnetwork can be thought of as one huge factory where parts/requisitions (jobs) need to be routed through different logistical nodes (ma-chines) to reach their ﬁnal destination (completethe job) by the established due date. There isextensive literature on the routing of parts in afactory to solve many different optimizationproblems. Most of the literature, however, doesnot address problems of the same scope and size as the Army logistical network. Few of the so- lution techniques in the literature can solve sucha problem or can come up with a good solutionwithin any reasonable amount of time. Oneadvanced analytics tool that can ﬁnd good so-lutions to very large scheduling problems in ashort period of time is the Virtual Factory(Hodgson et al ., 1998). The authors used revised slack and a repetitive deterministic simulation- based procedure to solve the N-job, M-machine,large job shop scheduling problem for mini-mizing maximum lateness ( L max)–t h e N/M/ Lmaxproblem—which is known to be NP-hard (Garey, et al., 1976). We introduce the VirtualFactory as applied to our logistical problem.Starting from the present state of the system (i.e., the location of every requisition in the system plus its residual routing), the VirtualFactory runs a simulation. Requisitions aresequenced at logistical nodes in order of in-creasing slack (the maximum time a requisi-tion can be in a queue at a node and still meetits due date for the ordering unit). Let d ibe the due date of requisition iand pijthe processing time of requisition iat logistical node j.T h e slack of requisition iat node mis then de- ﬁned as slack im¼di2X j2m1pij; where m1is the set of all operations along the route of requisition ithat are performed after completion of the requisition on node m. Since using slack to sequence a requisition does notaccount for potential queuing, revised slack(time at which a requisition needs to beginprocessing at the node to meet its due date) is used after the ﬁrst iteration to provide better results. Queuing times from the deterministicsimulation are recorded for each requisition at each node and then revised slack is calculatedby incorporating the queuing times. Let q ijbe the queuing time for requisition iat node j. Re- vised slack is deﬁned as slack9 im¼di2X j2m1pij2X j2m11qij; where m11is the set of all operations along the route of requisition ithat are performed after completion of the requisition at node mexcept the one immediately following m. The simula- tion is then run with requisitions sequenced ateach node in order of increasing revised slack.This process is repeated, using continuallyupdated revised slack, until an iteration limit is reached or the lower bound ( LB)o n L maxis achieved. The Virtual Factory was originallytested using data from a large furnituremanufacturing facility. The authors showed thatthe procedure achieved excellent solutions rel-ative to the LBonL maxin little computational time. They found that problems tend to stabilizeat an excellent L maxafter 10 or less iterations of the simulation. To represent the external transportation re- quired to run multifactory manufacturing sup-ply chains, Thoney et al .(2002) introduced the use of batch processors in the Virtual Factory.Batch processors are machines that processmultiple jobs at the same time and all jobs re-main inside the batch processor until the oper- ation is complete. This development permitted direct modeling of containers, trucks, cargoplanes, and ocean freighters. Although the Virtual Factory was initially created as an advanced analytics tool for solvinglarge job shop scheduling problems, it can beused to solve other large problems that have anetwork similar to a factory. The ﬁrst proposed use of the Virtual Factory to solve a military- related scheduling problem was the Dep-loyment Scheduling Analysis Tool (DSAT),created to help plan for Army deployments tooverseas countries (Trainor, 2001; Hodgsonet al., 2004). DSAT schedules the movement ofequipment from the unit’s home station to theﬁnal staging area in the forward deployment location. Where DSAT focuses on building the time phase force deployment data needed to Military Operations Research, V23 N4 2018 Page 7A MILITARY LOGISTICS NETWORK PLANNING SYSTEM synchronize the movement of a unit from home station to theater, this paper focuses on thesustainment challenges of meeting the needs of units in an operational theater. Forecasting is required to identify logistical problems in time to make effective interven-tions. The type of forecasting needed is mission-based forecasting. There is widespread literatureon forecasting but very little literature onmission-based forecasting because it is a rela-tively new topic. Current Army forecasting techniques involve averaging past demand over several years and across many different missionsets and geographical locations (Parlier, 2016).The large variability in such average rates limitsforecast accuracy for the multitude of differentmission sets and potential operating environ-ments faced by today’s Army. The Army needsforecasts that are unique to a speciﬁc type of unit, conducting a speciﬁc type of mission, in a speciﬁc location. Mission-based forecastinguses stratiﬁed sampling to create forecasts fordifferent mission sets in different environments.The concept of mission-based forecasting wasﬁrst introduced in a 2005 government consul-ting group study by the Logistics ManagementInstitute (LMI) and further articulated in “Transforming U.S. Army Supply Chains” (Parlier, 2011). Using Army aviation units as atest bed, LMI looked at repair part (US ArmedForces Class of Supply IX–Class IX) demand for AH-64 Apaches during training in the conti-nental United States (CONUS), major combat operations (MCO) in Iraq, and stability opera- tions (Stab) in Iraq. Looking at an AH-64 heli-copter unit in these three scenarios resulted indata that was broken down by mission type(Training versus MCO versus Stab) and envi-ronment (temperate CONUS versus Iraqi des-ert). Results of the research are presented inFigure 1 with part demands given as percent- ages in the Venn diagrams. The intersection of each Venn diagram represents the part demand during CONUSoperations and operations in Iraq. The aggre-gated demand for all of these parts (part depth)was 4.4 times higher during MCO in Iraq thanduring CONUS operations and 3.9 times higherduring stability operations in Iraq. Additionally, the Venn diagrams portray a large percentage of parts demand seen during CONUS operationsthat is never seen in Iraq and vice versa (partbreadth). The Department of Defense has many planning tools at its disposal and uses differentones depending upon the agency and type ofanalysis needed. Although each planning tool is useful and provides helpful analysis, none offer end-to-end analysis of the entire sustainmentsystem. In addition, none of the tools in the Figure 1. Part demand. Relative composition and magnitude for AH-64D Apache comparing CONUS part demands with major combat operations in Iraq (left) and stability operations in Iraq (right). Percentages are forpart demands (Parlier, 2011). Page 8 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM following list interfaces with an ERP system to exploit the existing logistical data. Many of thewell-known logistic planning tools have a focus outside of sustainment supplies and so will not be discussed. The following is a nonexhaustivelist of planning tools, but covers some of themore widely used tools or tools more closelyaligned with the capabilities of the MilitaryLogistic Network Planning System (MLNPS)presented in this article: •Analysis of Mobility Platform: AMP is the most similar to the MLNPS. Unlike many otherplanning tools found during research, AMPallows end-to-end modeling but requires se-quential tools to achieve this perspective. AMP is a modeling and simulation applica- tion used for analyzing the end-to-endtransportation of equipment, passengers,and supplies through the defense trans-portation system. AMP is used widely by theUS Transportation Command (TRANSCOM)in providing an understanding of the overalltransportation capabilities needed for long- term strategic planning (Anonymous, 2001). •Logistic Battle Command: LBC is a discrete event simulation capable of theater-levelmodeling and sustainment planning andveriﬁcation (TRAC-LEE, 2015). The model isused primarily by the Army Training andDoctrine Command’s Analysis Center at FortLee, Virginia. LBC is a highly ﬂexible model that can be adapted to help answer a multitude of logistical problems/questions. It can modellarge scenarios with as many as 18 brigadecombat teams (BCT) or help generate a scheduleof resupply convoys for a single BCT. •Planning Logistics Analysis Network : PLANS is a web-based logistics planning tool beingbuilt by the US Army Engineer Research and Development Center (ERDC). Although still in the design phase (at the time of thiswriting), the model has the potential to bepowerful in planning in-theater logisticaloperations. It is not an end-to-end model. Themodel takes a detailed, micro look at all in-theater logistical operations (personal com-munication from Corey Winton during 2015 visit to the ERDC). From weather pattern impacts on roads and soils, to the efﬁciencyof individual forklifts in a warehouse, thestrength of this model is in the details. Al- though a micro analysis of a network is im-portant, it is not the focus of the MLNPS. MODELING THE NETWORK The path of a requisition transiting the military supply network includes handling at numerous logistical nodes and transit on mul-tiple transportation assets. Handling at a logis-tical node includes operations such as pulling apart off a shelf at a supply warehouse (pick-and-pack operations), palletizing/containerizingsupplies for air/sea movement, and break-ing down pallets and separating by ordering unit. The combination of supply node, demand node, and mode of transportation determineswhich logistical nodes and transportation assetssupply transit. Each node is modeled as a ma-chine in a factory where jobs (requisitions) arerequired to queue until the machine becomesavailable at which time they are processed withdeterministic processing times. The logistic nodes and transportation assets are referred to as processors and are assigned a processornumber. Each processor number is assigned aprocessor type, where each type represents aspeciﬁc function within the logistical network.Figure 2 shows the path a requisition must takefrom point of supply to point of consumption inIraq. The Iraq network is shown here because it will be used later to validate the model, but similar networks exist for other customersthroughout CONUS and the world. When a requisition is entered into GCSS- Army, there is a period of time when the De-fense Logistics Agency (DLA) determines theproper mode of transportation, and from whichdistribution center (DC) the requisition should be sourced. This is called the order-and-source time. Once a requisition is sourced to a speciﬁcDC, those supplies must be pulled from thewarehouse shelves and packaged (usually in amultipack box) for shipment (pick-and-packoperations). Requisitions that are sourced at aDC not co-located with the consolidation andcontainerization point (CCP) must be shipped on a commercial truck to the CCP. Once at the CCP, requisitions/multipacks are loaded onto463L pallets and, if being routed by sea, packed Military Operations Research, V23 N4 2018 Page 9A MILITARY LOGISTICS NETWORK PLANNING SYSTEM into 40-foot containers. Pallets and containers are shipped from the CCP to the airport of em-barkation (APOE) or seaport of embarkation (SPOE) on commercial trucks. Once at the APOE (SPOE), pallets and containers are loaded ontoaircraft (ships) as they become available. Whenthe pallets (containers) arrive at the port of de-barkation (APOD/SPOD), they are downloadeda n dp r e p a r e df o rm o v e m e n tt ot h et h e a t e rd i s t r i -bution center (TDC). The containers and palletsare then moved to the TDC via both commercial and military trucks depending upon availability. Operations at the TDC consist of breaking downpallets and containers and re-sorting requisitionsby unit. Supplies are moved between the TDCand the ordering unit with military trucks onlybecause the roads are in a combat zone. This ﬁnalmovement of supplies is commonly referred to asthe last tactical mile (LTM). Each of the logistical elements seen in Figure 2 is represented by one of seven differenttypes of Virtual Factory processors. Each pro-cessor has unique characteristics but can rep-resent more than one type of logistical elementin the network. These processors govern thelogic followed by the model at each logisticalnode in the network. The processors allow for the modeling of order fulﬁllment at a distribu- tion center, aggregating multiple shipmentsonto a pallet, loading a shipping container,transporting with a single truck, or breaking down containers and pallets prior to ﬁnal de-livery. These processors form logical building blocks with which we describe and model each logistical node in the network. The logistical network is modeled using a processor for each logistical node and trans-portation asset. Each requisition is assigned apath of processors it must transit, simulating thepath it must take in the logistical network. Eachrequisition is also given a due date based on the ﬁnal destination and priority in accordance with Army regulations (for more detail, see USDepartment of the Army [1995, 1997] andRogers [2016, section 3.2]). Processing times areassigned for each processor to represent thetime it takes to process a requisition at each lo-gistical node, though requisitions will also ex-perience waiting times as they queue for each processor. The model requires a mission-based forecast for the sustainment supplies neededfor an operation to simulate the movement ofthe supplies through the network using theVirtual Factory’s sequencing procedure. Net-work details and underlying assumptions canbe found in Rogers (2016, section 3). The setup and operation of the MLNPS is represented in Figure 3. When a contingency mission is identiﬁed, the composition of the lo-gistical network must be identiﬁed and created Figure 2. Requisition path from the Continental United States to Iraq. Page 10 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM using processors (box 5.2). Coupled with the existing, “Status Quo,” network (box 5.1) andshipping capabilities and constraints (box 5.3),the logistical network structure is created. Themission-based forecast consists of the homestation, “Status Quo,” units (box 2.1) and theunits conducting the contingency mission (box2.2). This mission-based forecast and the parts already transiting the system (box 4), obtained from ERPs, make up all of the sustainmentrequisitions to be simulated. The networkstructure and requisitions are entered into theVirtual Factory (box 6) and the simulation isrun. Choke points are identiﬁed by the amountof queuing at logistical nodes. A decision makermust then determine if the queuing is accept- able or if the network capabilities need to be adjusted to reduce queuing. If the decisionmaker is satisﬁed (box 8), the logistical planningis complete and required capabilities are iden-tiﬁed. If not satisﬁed (box 7), the networkstructure is adjusted (i.e., assets are added to thenetwork) and the simulation is run again. Thisprocess of observing the queuing time and adjusting the network continues in an iterative manner until the decision maker is satisﬁedwith the logistical plan.Once the Virtual Factory was adapted to meet the needs of the MLNPS, the model wasvalidated by comparing the model results todata from an actual network. This was done bysimulating the movement of the requisitionsduring OIF and comparing the actual queuingseen in the network (as recorded by the RANDstudy) with the queuing from the model. The data set that was used in the 2005 RAND study (Peltz et al., 2005) was obtained. The dataset hadall requisitions (years 2003–2004) from OIF asthe data points on network performance intransporting those requisitions through thenetwork. Only a portion of the OIF dataset wasused when validating the model. Requisitionswith release dates two weeks prior to forces crossing into Iraq through the end of May 2003 were simulated. This sub-dataset consisted ofapproximately 1.96 million requisitions withrelease dates spanning an 87-day period. Wefocused our analysis on the ﬁrst 12 weeks of thewar because after that amount of time, the forcestructure, geopolitical situation, mission set, thedisposition of enemy and friendly forces, and even the logistics system changed signiﬁcantly. Approximately 647,000 of these requisitionswere destined for units in Iraq or Kuwait, with Figure 3. Military Logistics Network Planning System ﬂow chart. Military Operations Research, V23 N4 2018 Page 11A MILITARY LOGISTICS NETWORK PLANNING SYSTEM the remaining approximately 1.3 million requi- sitions destined for units in CONUS or else-where in the world. The 1.3 million requisitions not going to OIF were included in the simu- lation because they went through the sameprimary distribution center, Defense DepotSusquehanna, Pennsylvania (DDSP), as thosethat went to OIF and thus they competed for thesame resources. The dataset did not include Class I (food and water) and Class V (ammunition). Since CL I and CL V supplies use a signiﬁcant amount of transportation assets, they had to be added tothe model. Estimates for these supplies werecalculated using planning factors found inArmy ﬁeld manuals and added to the requisi-tion forecast. Before continuing, it is important to distin- guish between what will be referred to as “ac- tual” conditions and “originally planned” conditions. The logistical plan for OIF and whatactually happened differ in multiple areas. Forexample, during planning, l ogisticians determined that 965 (“originally planned”) military truckswere needed to transport supplies from the TDCin Kuwait to troops in Iraq (Peltz et al., 2005).When forces crossed the line of departure into Iraq, there were only 191 (“actual”) trucks available. Other planned versus actual numbers as well ashow the values were used in the model can befound in Rogers (2016). The model was run with “actual” planning conditions and compared to how the networkactually performed (reality). This is done bycomparing the model results with either the empirical evidence from the OIF dataset or us- ing anecdotal evidence from OIF after-actionreviews (AARs). Queuing at critical nodesalong the network path, found in Figure 2, wascompared. All of the nodes were analyzed butonly those four with measurable queuing arediscussed. The ﬁrst two nodes are comparedusing empirical evidence from the OIF dataset. The ﬁrst statistic compared is the pick-and-pack time at the DDSP, the primary distributioncenter for supplies going to OIF. Figure 4 showsthe by-month average pick-and-pack time com-parison for March to May 2003. The average pick-and-pack time from the model and dataset are quite similar, with amaximum difference of 0.24 days for anymonth. Considering the smallest time unit of the simulation is days, this shows that the modelcan very closely represent what really hap-pened at DDSP. The next node that had signiﬁcant queuing was the APOE. Figure 5 shows a comparison of by-month average time in the queue at the APOE. The difference in APOE queue time be-tween the model and dataset is more signiﬁcantthan at DDSP, but is no more than a little over aday for any of the three months. The last two nodes discussed are compared using anecdotal evidence, because the in-theater queue times were not available in the Figure 4. Monthly average pick-and-pack time at the Defense Depot Susquehanna, Pennsylvania. Figure 5. Average time in queue at the airport of embarkation. Page 12 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM OIF dataset due to the difﬁculties in tracking the supplies once they arrived in Kuwait. The an-ecdotal evidence comes from AAR comments stating that repair parts did not start arriving, uninterrupted, into Iraq until around May 1,2003 (Fontenot, 2005). The ﬁrst node that showed signiﬁcant queuing during the ﬁrst 90 days of OIF was theTDC in Kuwait. Figure 6 shows the by-day av-erage time a requisition was in queue at the TDC. In addition to the by-day average time in the queue, Figure 6 also shows the stafﬁng level of the TDC with a secondary axis and line plotsuperimposed on the graph (Peltz et al., 2005).The day that coalition forces crossed the line ofdeparture from Kuwait into Iraq is also depictedon the graph to understand when forces wereactually in Iraq waiting for supplies. The starmarks the approximate day requisitions began consistently arriving at ordering units. It is im- portant to point out that queue times do notinclude any requisitions for CL I or CL V. The graph shows that there is no signiﬁcant queuinguntil a little over a week after forces entered Iraq. This can be explained by the fact that few supplies were ordered until forces had a tacticalpause in ﬁghting and were able to put supplieson order. Once the ordering began, around theend of March, requisitions were in the TDCqueue for a signiﬁcant amount of time. Thissigniﬁcant queuing can likely be explained bythe fact that there were only 200 personnel working in the TDC and their primary focus was getting CL I and CL V into Iraq. The modelhad queuing that reached nearly 45 days beforethe TDC was able to start working off thebacklog and reduce the queuing time. The pointat which the model begins to show a signiﬁcantdrop in average queue time coincides with theMay 1 date that AAR comments suggested parts began arriving uninterrupted to forces in Iraq. Although a direct comparison to the actual Figure 6. Average time in queue at the theater distribution center (TDC). Military Operations Research, V23 N4 2018 Page 13A MILITARY LOGISTICS NETWORK PLANNING SYSTEM queuing at the TDC was not possible, the queuing from the model mimics what actuallyhappened based on the available anecdotal evidence. The last node that showed signiﬁcant queuing during the ﬁrst 90 days of OIF was theprocessor that represented the LTM trucks.Figure 7 shows the by-day average number ofrequisitions waiting to be moved by the LTMtrucks. In addition to the by-day number of requi- sitions in the queue for LTM trucks, Figure 7 shows the number of LTM trucks on hand foroperations with a secondary axis and line plotsuperimposed on the graph (Peltz et al ., 2005). The graph shows that from the point whenforces crossed into Iraq, the number of requisi- tions in the queue increased steadily until ap-proximately April 10. A series of events contributed to the extensive queuing. First, as already discussed, 191 of the required 965trucks were available when forces crossed intoIraq on March 20, 2003. Next, on March 23, asandstorm crippled logistics for a three-dayperiod (Peltz et al ., 2005). No trucks or aircraft were able to provide any type of resupply totroops in Iraq. When the sandstorm ﬁnally cleared on March 26, the LTM trucks were re-tasked tomove troops. Once trucks were ﬁnally availablefor resupply operations on March 31, troops inIraq were in dire need of CL I and CL V (food, Figure 7. Average number of requisitions in queue for last tactical mile trucks. Page 14 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM water, ammunition) resupply. To bring unit on- hand supply up to the appropriate levels, theavailable LTM trucks were ﬁlled almost exclu- sively with CL I and CL V. This helps explain why the backlog of requisitions was not imme-diately worked off in the model. Analysis withthese model inputs indicates that sufﬁcient LTMcapacity for CL IX was not achieved until ap-proximately April 11. This did not, however,lead to an uninterrupted delivery of supplies.The TDC was still not operating at full capacity on April 11 (see Figure 6), which resulted in requisitions only trickling out until approxi-mately May 5. This is the point at which themodel shows uninterrupted delivery of requi-sitions to units in Iraq. Just as with the TDCqueuing, LTM truck queuing from the modelmimics what actually happened based on theavailable anecdotal evidence. USING THE MLNPS TO PLAN OPERATION IRAQI FREEDOM To demonstrate the MLNPS’ utility in planning the logistical network for a contin-gency operation, the analysis required the OIF dataset as an input. Since many of the planning factors for OIF are known, the MLNPS was usedto model the OIF network under originallyplanned conditions. The originally plannedfactors are what OIF planners determined wereneeded, using the planning tools they had at thetime and the tactical plan for OIF. The modelwas run with the originally planned input values and the network performance was ana- lyzed. The network performance from themodel was compared to how the network wasperforming, in terms of queuing, prior to thestart of OIF. For the ﬁrst 90 days of operations,the model did not show signiﬁcant queuing atany node. This shows that if the OIF logisticalnetwork had been established as planned, it would have run without any excessive queuing for the ﬁrst 90 days of operations. The modelwas then run for the ﬁrst 365 days of the oper-ation, using all of the requisitions from 2003(;7.67 million requisitions). Signiﬁcant queu- ing was observed for the pick-and-pack opera-tions at DDSP. The queuing did not occur in theﬁrst 90 days, but shortly after began to steadilyincrease until queuing exceeded 25 days in December 2003. Figure 8 shows the queuingthat the model predicted. As planned, it was thought that DDSP ca- pacity at the start of OIF would be sufﬁcient tohandle the requirements of OIF. What actuallyhappened? At some point during OIF, DDSPreceived indicators that the demand was ex-ceeding the capacity. DDSP had to hire 400 ad-ditional workers to work off the requisitionbacklog and prevent further backlogs (Peltz et al., 2005). The indicators were not seen with enough time to hire the 400 workers needed toprevent signiﬁcant queuing. By the time theadditional 400 workers were able to startworking off the backlog of requisitions, thequeuing had reached almost 12 days by August.Figure 9 shows the comparison of monthlyqueuing predicted by the model (without any increase in capacity) and the actual queuing from the OIF dataset (with the increase in ca-pacity from 400 additional workers). As Figure 9 shows, the hiring of 400 addi- tional workers resulted in DDSP eventuallygetting the queuing under control by the end of2003; however, the peak queuing of 12 days wasunacceptable and had profound downstream network impacts (Peltz et al ., 2005). Ideally, DDSP should get bottleneck indicators earlier soit can hire workers in time to prevent queuingfrom exceeding three to four days. The resultsshow that the MLNPS tool can forecast signiﬁ-cant queuing in time for capacity to be properlyadjusted. The MLNPS was run with adjusted capac- ities until the queuing was at acceptable levels for the duration of 2003. Figure 10 illustrateshow capacities were increased iteratively toachieve these acceptable levels of queuing; theright graph shows the third iteration resultingin the ﬁnal solution. After three iterations of capacity adjust- ments, the model queuing appears to be under control. With additional capacity of 3,000 req- uisitions per day (15 percent increase in capac-ity) added in May and 500 added in November,DDSP would be able to handle the volume ofrequisitions associated with the increase ofBCTs ﬁghting in Iraq. There could be more ef-ﬁcient solutions, and working with DDSP toplan a more phased hiring plan would almost Military Operations Research, V23 N4 2018 Page 15A MILITARY LOGISTICS NETWORK PLANNING SYSTEM certainly be better than the incremental capacity changes used in the three iterations. This does,however, illustrate how the MLNPS could beused to forecast and then eliminate queuing at a distribution center. What is not shown, but cannot be ignored, is the potential downstreamnetwork improvements created by reducedqueuing at DDSP. The MLNPS could also have been used to determine how many trucks could be cut fromthe originally planned 976 trucks and still sus-tain OIF combat operations. To demonstrate this, the model was initially run with 200 LTM trucks (197 trucks were actually present).Figure 11 shows that signiﬁcant queuing occurswith only 200 trucks. When the number of LTM trucks is in- creased to 300, there is no signiﬁcant queuing,indicating that 300 trucks would be sufﬁcientfor the ﬁrst 90 days of operations (see Figure 12). Knowing that 300 trucks was the minimum needed for the start of operations, plannerscould have ensured that the extra 100 truckswere not delayed in arriving to Kuwait but still diverted the remainder with conﬁdence. With the MLNPS validated as a credible tool for predicting performance of the Army’s logistical Figure 8. Average pick-and-pack times at Defense Depot Susquehanna, Pennsylvania, for 2003 according to model (as planned). (OIF: Operation Iraqi Freedom) Figure 9. Average pick-and-pack time at Defense Depot Susquehanna, Pennsylvania, as planned versusactual OIF. Page 16 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM system, the next step is to show how it could be used to help plan contingency operations. USING THE MILITARY LOGISTICS NETWORK PLANNING SYSTEM TOPLAN A NOTIONAL OPERATION To use the MLNPS to plan the logistical network for an expeditionary operation, anotional operation had to be created. The background for the notional operation is that the Islamic State in Iraq and Syria (ISIS) has moved equipment and ﬁghters from Libya intothe bordering country of Sudan. The Sudanesegovernment is harboring these ﬁghters and fa-cilitating the planning and execution of attackson bordering countries. ISIS is using the safehaven in Sudan to stage attacks in other coun-tries in Africa, mainly in South Sudan. The in- ternational community has condemned the Figure 10. Average pick-and-pack time at Defense Depot Susque hanna, Pennsylvania, with added capacity. (a) First iteration: 12,000 requisition capacity/day in May; (b) Final: 13,000 requisitions/day starting in May and 1500 requisitions/day starting in November, R1: 12,000 requisitions capacity/day in May, R2: 13000 requisitions capacity/day in May, R3: 13,000 requisitions capacity/day in May and 1500 requisitions capacity/day in November. Figure 11. Number of requisitions in queue for 200 last tactical mile (LTM) trucks. Military Operations Research, V23 N4 2018 Page 17A MILITARY LOGISTICS NETWORK PLANNING SYSTEM actions of the Sudanese government in harbor- ing ISIS. The United States sees the presence ofISIS in Sudan as a threat to African allies and a tremendous instability in the region. The United States has committed to helping protect SouthSudan and other African allies. The mission forthe US is to destroy ISIS troops and equipmentin Sudan in order to prevent their ability toconduct attacks in Africa. With the concentration of ISIS ﬁghters in the capital of Khartoum and the impoverished Darfur region, the mission requires two US BCTs. The plan calls for one infantry BCT (IBCT)and one Stryker BCT (SBCT). The SBCT willconduct operations in the Darfur region and theIBCT will operate in Khartoum. Table 1 presentsthe entire task organization (troops required)for the operation. This task organization is mainly used to create the requisition forecast in lieu of an actual mission-based forecasting. The path a requisition travels for the Sudan operation is similar to OIF except for thegeographical differences once the suppliesg oo v e r s e a s .F i g u r e1 3s h o w st h ep a t ho farequisition. There are many ways to get forces into Sudan, but for this notional operation the SPOD is the port of Mombasa, Kenya. The TDC isestablished in South Sudan, which borders thehostile nation of Sudan. It is assumed that the truck network in Africa is not sufﬁcient totransport the shipping containers; therefore, military trucks must be used for all trucking operations. The APOD is located in SouthSudan and in close proximity to the TDC. Because there is no mission-based forecast for this notional operation, nor is there a datasetto act as a surrogate, a forecast had to be gen-erated. The forecast was generated using nu-merous properties of the OIF dataset. For each characteristic, a unique distribution was ﬁt us- ing Stat::Fit /C210. When there was a correlation be- tween two characteristics, the Gaussian copulamethod (Pham, 2006) was used to allow forgeneration of two characteristics while main-taining the correlation between the two. Figure 12. Number of requisitions in queue for 300 last tactical mile (LTM) trucks. Table 1. Notional Sudan mission task organization. Task organization 1 x Infantry Brigade Combat Team 1 x Stryker Brigade Combat Team 1 x Engineer Battalion (construction effects) 1 x Sustainment Brigade1 x Aviation Battalion (attack and lift) 1 x Division Headquarters 3 x Battalion miscellaneous enablers Page 18 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM Up to this point, everything modeled was assumed to be deterministic. Because of the deterministic nature of the models, single data points were used during analysis of the net-works. Random generation of the requisitionforecast introduces a stochastic element to themodel. Because single data points cannot neces-sarily be used with stochastic modeling, analysishad to be done to determine the number of datapoints needed to provide reliable results. Five forecasts were generated and simulated, and the variability in queuing at each of the critical nodeswas analyzed. For each critical node, all ﬁve datapoints were compared. The results showed thatacross all critical nodes, the maximum half-width for queuing time for any day at anynode was 0.336 days or just over eight hours.Considering the smallest time unit of the simu- lation is days, a maximum half-width of eighthours is not signiﬁcant. Thus, all further analysis was done using a single data point. Because of the long distance between the SPOD and units in Sudan, network planningincludes looking at two different locations forthe TDC. The ﬁrst location for the TDC is in thecapital of South Sudan, Juba. Placing the TDC inJuba decreases the distance between the SPODand TDC, but increases the distance betweenthe TDC and the units in Sudan. Placing the TDC in Juba will be referred to as Course of Action (COA) 1. The alternate location for theTDC is in the city of Wau (referred to as COA 2),which is closer to the border with Sudan, de-creasing the distance between the TDC and theunits in Sudan but increasing the distance be-tween the SPOD and TDC. Each COA has distinctly different travel times for the trucks transporting supplies. Figure 13. Requisition path from the Continental United States to Sudan. Table 2. Round-trip convoy times for each course of action (COA). Travel leg COA 1 (Juba) COA 2 (Wau) SPOD to TDC 7 days round-trip 10 days round-trip TDC to IBCT in Sudan 10 days round-trip 8 days round-trip TDC to SBCT in Sudan 8 days round-trip 8 days round-trip Notes: IBCT: infantry brigade combat team, SBCT: Stryker brigade combat team, SPOD: seaport of debarkation, TDC: theater distribution center. Military Operations Research, V23 N4 2018 Page 19A MILITARY LOGISTICS NETWORK PLANNING SYSTEM Round-trip times for convoys vary between six and 10 days. The travel time was determinedusing the road distance from www.google.com/ maps, and a march rate of 30 kilometers per hour. Table 2 summarizes the round-trip timesused for each COA.For each COA, the MLNPS was used to determine the capacities needed for each of thecritical nodes. The critical nodes analyzed for this operation are DDSP, APOE, APOD, and SPOD trucks, TDC, and LTM trucks. Once theMLNPS is used to determine the capacitiesneeded at each critical node for each COA, theCOAs are compared and a COA is recom-mended to the commander. When determining the capacity needed at each node, a decision maker would determine the amount of queuing that is acceptable at each node. In lieu of a decision maker, historical av-erage delay times were used. Table 3 displaysthe results of COA 1 analysis by listing the re-quired capacity for the ﬁve critical nodes. The process of determining the capacities needed at the APOD and SPOD truck and LTMtruck nodes are next presented to show how the MLNPS can be used to plan. First the required number of APOD and SPOD truck companiesTable 3. Course of action (COA) 1 required capacities. Critical node COA 1 capacity * DDSP 2,100 requisitions/day APOE 25 pallets/day APOD and SPOD trucksFive truck companies TDC 500 pallet equivalents/day LTM trucks 7 truck companies * The unit of capacity is speciﬁc to each node. Notes: APOD: airport of debarkation, APOE: airport of embarkation, DDSP: Defense Depot Susquehanna, Pennsylvania, LTM: last tactical mile, SPOD: seaportof debarkation, TDC: theater distribution center. Figure 14. Seaport of debarkation queuing for course of action 1. Page 20 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM was determined by iteratively increasing the number of truck companies until queuing wasat an acceptable level (see Figure 14). With ﬁve truck companies, the queuing is reduced to an average of approximately threedays for the duration of the operation. It ap-pears now that the queuing is due to theround-trip time of the trucks, not due to in- sufﬁcient capacity on the trucks when they depart. In the same iterative manner, the required number of LTM trucks was determined (seeFigure 15). Each BCT was looked at separatelybecause they are in two different locations. With seven truck companies, queuing is reduced for both BCTs to a level of queuing that appears to be because of round-trip time. The recommended number of truck companies todedicate to LTM operations is therefore seven.The same procedure was done for COA 2 with the results found in Table 4. As expected, the number of APOD and SPOD truck companies increased and the Figure 15. Last tactical mile queuing for course of action 1. Table 4. Course of action (COA) 2 required capacities. Critical node COA 2 capacity DDSP 2,100 requisitions/day APOE 25 pallets/day APOD and SPOD trucks7 truck companies TDC 700 pallet equivalents/day LTM trucks 6 truck companies Notes: APOD: airport of debarkation, APOE: airport of embarkation, DDSP: Defense Depot Susquehanna, Pennsylvania, LTM: last tactical mile, SPOD: seaport of debarkation, TDC: theater distribution center. Military Operations Research, V23 N4 2018 Page 21A MILITARY LOGISTICS NETWORK PLANNING SYSTEM number of LTM truck companies decreased. This is logical because the only differences in thetwo networks are the locations of the TDC. With a combatant commander looking at two different COAs, they will receive a recom-mendation from each of the staff sections thatrepresent the warﬁghting functions. For exam-ple, the staff section representing the protectionwarﬁghting function would probably recom-mend the COA that allows the forces to mosteasily secure the TDC and the convoys moving between it. For logistics, one has to look at the equipment and personnel needed to support aspeciﬁc COA. A comparison is conducted todetermine which COA is better. Table 5 showsthe COA comparison with only those criteriathat differ between the COAs. COA 1 is clearlythe recommended option, outperforming COA2 in each of the three distinguishing categories. USING THE MILITARY LOGISTICS NETWORK PLANNING SYSTEM TOPERFORM “WHAT IF” ANALYSIS Prior to the COA decision process, the MLNPS can be used to see how robust the plan is to po-tential changes. We chose to analyze the SPODtrucks to determine how sensitive the requirednumber is to the extreme case of a disruption of thesupply lines where the trucks are unable to movefor seven days. This may be due to weather, a damaged bridge, enemy action, or many other potential reasons. Figure 16 shows the impacts of as e v e n - d a yd i s r u p t i o no nt h eS P O Dt r u c k s . The disruption causes an initial spike in queuing of approximately three days and thenlevels off to a near constant increase of one day.Because the queuing never returns to predis-ruption values, a reasonable conclusion is that the number of SPOD trucks does not make thenetwork robust enough to handle a seven-day disruption of the supply lines. If the decisionmaker agreed with this assessment, the number of SPOD truck companies would be increased and the sensitivity again tested for robustness. Thistype of analysis could be done at any of the nodesor for alternate “what if” questions (Rogers [2016,section 6.4] provides more examples). In this article, the MLNPS was used only to plan the logistical requirements for an opera-tion. It could also be used during an operation. It could be used to predict the impact of different operational scenarios on the logistical network.If, for example, a storm powerful enough to shutdown air and ground logistical operations wasapproaching the area of operations, the MLNPScould be used to predict the impacts on the lo-gistical network. The impacts could be presentedto the commander, giving time to either adjust the logistical network or take other measures to mitigate the negative impacts. SUMMARY The model presented in this article looks at the end-to-end movement of sustainment su-pplies needed for supporting expeditionary operations. By modeling the repair part requisitions as jobs to be processed in a factory,we used an efﬁcient scheduling heuristic toobtain a forecast for logistical performance. Wedemonstrate that this method is capable ofpredicting the location, timing, and magnitudeof logistical backlogs (excessive queueing) us-ing both data from OIF and a notional scenario. We also present a simple method to (1) de- termine the required capacities through the lo-gistics network, and (2) assess the impact ofa network disruption and the subsequentrecovery. Table 5. Course of action (COA) comparison. Comparison criteria COA 1 (Juba TDC) COA 2 (Wau TDC) Required truck companies 12 truck companies 13 truck companies TDC capacity 500 pallet equivalents 700 pallet equivalents Average total queuing (in theater) 10.4 days 14.6 days Note: TDC: theater distribution center Page 22 Military Operations Research, V23 N4 2018A MILITARY LOGISTICS NETWORK PLANNING SYSTEM Most importantly, this paper contributes a proof of concept for a sustainment planning and decision-support tool that harnesses the Army’sERP data and provides relevant information in atimely matter. To our knowledge, no other toolfeatures this exploitation of real-time systemdata. We conclude that however useful thismodel is, GCSS-Army itself holds tremendouspotential for analytical tools to assist military planners and decision makers. ACKNOWLEDGEMENTS The authors thank Mark Robbins from RAND, Joseph Faris from DLA Distribution,and LTC John Hiltz & Team from USTRANSCOM. This paper would not have been possible without their assistance. The authors also thankthe skilled professionals at ERDC, TRAC-LEE,and TRAC-MRY for taking time to showcasetheir analytical tools as well as provide invalu-able feedback for both this paper and our mili-tary logistics research.