Driving The Design Of ASRS Solutions With Data

WHITE PAPER / HOW DATA DRIVES DESIGN OF AUTOMATED STORAGE AND RETRIEVAL SYSTEMSDRIVING THE DESIGN OFASRS SOLUTIONS WITH DATABYAlfredo ValadezData is vital when considering a warehouseautomation project, but it can be unclear howdata drives the design of automated storageand retrieval systems. Though every supplier’smethodology is unique, historical data playsa crucial role in a cost-effective design.

WHITE PAPER / HOW DATA DRIVES DESIGN OF AUTOMATED STORAGE AND RETRIEVAL SYSTEMSBig Data, the internet of things, Factory 4.0. There’sdemand during holidays, while other businesses mightno shortage of jargon relating to current data trends.be dependent on promotions driven by marketing.Essentially, though, all of these terms refer to the useof data to make better objective decisions.Successful ASRS design depends on a thoroughunderstanding of rate requirements, inventory, stockToday, data-driven decision-making is used in industrieskeeping unit (SKU) profiles and space requirementsranging from retail and banking to healthcare andthroughout this cyclical pattern. Accordingly, at least onemanufacturing. While it is often most closely associatedfull year of data is essential to the design of the ASRS.with increasing sales and revenue, it is equally relevant forthe design of automated systems in both manufacturingDESIGN OF THE SYSTEMfacilities and warehouses. Specifically, historical andData relating to an order volume profile plays a key roleoperational data should drive the design of everyin determining the technology needed to optimize theautomated storage and retrieval system (ASRS).output of the system. Orders can be fulfilled at differentpicking units, such as a full pallet, a layer, a case or evenOn warehouse automation projects, “We need data” isa single unit. Understanding the volume of each of thea common refrain. But it is less clear how data actuallyunits picked throughout the year is critical to selecting theleads the design of the system. This paper will provide aright technology. The data also must be broken down togeneral overview of how data drives ASRS design, althoughthe rate required per hour, because this is how equipmenteach technology supplier or integrator will use its ownis specified.design methodologies and conduct a deeper assessmentinto the variables that affect the successful design ofindividual systems.TERMS YOU SHOULD KNOWANALYZING HISTORICALFULFILLMENT VOLUMEThe initial step in designing the system is analyzinghistorical fulfillment data. Decisions regarding what toFirst, clarity of terms is needed. The term ASRS is oftenautomate should be driven mainly by savings in operatingused to refer to automated warehouses, because productscost, improved efficiency and reduction of physicallyare stored or retrieved automatically from locations withindemanding tasks performed by operators. Other internalthe building. The term also can be used to describe thefactors also may be considered.actual robots that retrieve products from racks. In thispaper, ASRS refers to the entire warehouse system.Figure 1 shows the fulfillment volume by picking unit fora warehouse that fills orders in pallets (or unit loads),ASRS suppliers commonly talk about the design of thelayers and cases.system and sizing of the system. Design of the systemrefers to the process of selecting the most appropriatetechnology, such as a unit load (UL) crane, robotic layerpicker or automated guided vehicle (AGV). After thetechnology has been identified, the quantity of each ofthe technology products has to be determined. This isknown as sizing the system. Both design and sizing areguided by data.A YEAR’S WORTH OF DATAMost businesses have cyclical demand, meaning theydon’t fulfill orders consistently throughout the year. Forexample, hot beverage suppliers experience high demandduring the winter, while those who sell cold beverageshave high demand in the summer. Many retailers have high 2021CasesLayerPalletsFIGURE 1: Fulfillment volume by picking unit. This sample data indicates the needfor an ASRS solution that prioritizes the picking of pallets.PAGE 2 OF 5

WHITE PAPER / HOW DATA DRIVES DESIGN OF AUTOMATED STORAGE AND RETRIEVAL SYSTEMSOver the course of the year, about 65% of the total volumewas picked as pallets. About 30% was picked as layers,and the remainder was picked as cases. Therefore, thesolution for this warehouse should prioritize automatingthe picking of pallets because it makes up the highestoperating cost.This data profile is typical for a warehouse attached toa manufacturing facility. By comparison, the data for aretailer distributing to smaller stores or distribution centers(DCs) would likely be reversed. In that situation, casesFIGURE 2: Full pallet orders; 3,000 pallets per day represents the 90th percentile ofdaily volume at this distribution center.would make up approximately 65% of the total pickingvolume, and the solution would need to be optimized forpicking cases.SIZING OF THE SYSTEMNext, you’ll need to evaluate the volume for each of thesepackage types to determine if automation is the rightsolution, and if so, which technology is most appropriate.To accomplish this, the volume of product shipped mustbe broken down to a daily rate and then an hourly rate,which is called the throughput of the system. There arevarious approaches to calculating throughput.One method uses the average volume per day. This isa cost-effective approach, but when there are spikes indemand, a system sized on the average won’t be able tokeep up. Some owners might prefer this method to reducethe initial capital investment. Typically, they would plan tosupplement the system with manual labor during periodsof peak demand.Another method is to size based on the peak daily volume.In this case, you know the system will be able to keep upwith peak demand, but it will be more expensive to buildand will be underutilized when not operating at peaklevels. Owners prefer this approach when the return oninvestment (ROI) for even this larger investment is withintheir payback policy.A third option is to try to balance the peaks and valleysto cover the highest percentile of the volume seenthroughout the year. Budget constraints, space availabilityand ROI goals also will influence what percentile willbe used. 2021A COMMON APPROACH:THE 90TH PERCENTILEIn the material handling industry, it is common to size theASRS solution to the 90th percentile of the daily volume.A manual process can be set up as a contingency forpeaks, or the system simply can be run longer.Figure 2 represents an example of an analysis done forpallet orders for a distribution center. It depicts the volumeof daily pallet orders across seasons. Note that althoughdaily volume is low at the beginning of the year, demandramps up as the year progresses.In this example, sizing the system to the 90th percentilewould mean using the rate of 3,000 pallets per day.This volume would then be divided by the number ofhours of operation per day. There is no hard rule on thenumber of hours to use in this calculation, since manyvariables specific to each application must be considered.System throughput requirements need to account forinbound material into the system (replenishment of thesystem), shipping and production schedules, and orderfulfillment demand schedules.Equipment maintenance and peak volume also should beconsidered. While the best ROI for the system is achievedwhen the equipment runs 24 hours per day, it is betterto size the system based on 20 or 21 hours per day toallow for regular maintenance and additional capacity.In this instance, if the daily rate of 3,000 pallets per daywere divided by 21 operating hours per day, the systemthroughput required would be 143 pallets per hour.PAGE 3 OF 5

WHITE PAPER / HOW DATA DRIVES DESIGN OF AUTOMATED STORAGE AND RETRIEVAL SYSTEMSTECHNOLOGY SELECTIONFOR PALLET PICKINGOnce you know the hourly throughput requirements,you can select the technology that best fits theapplication. Figure 3 shows general guidelines for theselection of different types of pallet ASRS based onthroughput and number of SKUs.A system that handles fewer than 200 SKUs usuallyrequires one type of crane technology, while a systemthat handles more than 1,000 SKUs requires another.Each circle shows arrows pointing outward, because noabsolute number determines the technology used.Once the ASRS type is selected, the “size” of the systemcan be determined by dividing the throughput of onedevice by the total rate required. In the previous example,Single Crane UL ASRSAisle Changing ASRSUL Shuttle and MoleAGVMole on ASRSFIGURE 3: Unit load ASRS general selection based on SKU count and throughput.System throughput and SKU count are important considerations when selectingpallet-picking technology.the SKU count is around 600. Therefore, a UL crane is thebest technology choice. The typical dual cycle rate for aIt’s important to note that this is a simplified example. InUL ASRS is around 50 pallets per hour. Because systema real sizing exercise, other variables would be taken intothroughput is 143 pallets per hour, at least three cranesaccount, such as space availability, inventory requirementswould be required to meet the system throughput.and specifics related to the product being handled.Keep in mind that the system should never be designed at itsTECHNOLOGY SELECTIONFOR CASE LEVEL PICKINGpeak rate. Instead, it should be designed for between 85%and 90% utilization to make sure the system has capacity forpeaks and catchup in case of downtime.The data analysis used to select the technology for pickingsingle cases is very similar to that used for picking pallets.Several different types of storage and retrieval technologycan be used, and Figure 4 shows which are mostDUAL OR SINGLE CYCLE?Dual cycle and single cycle refer to how a craneis loaded when putting or getting material. Ifthe crane puts a UL onto the rack but doesn’tretrieve a UL on its way back, that is a singlecycle. If it puts away a UL and retrieves anotheron its way back, that is a dual cycle.System design should maximize the numberof dual cycles in order to optimize throughputper crane moves. Unfortunately, this is notalways possible due to different shifts fororder fulfillment and replenishment, as well asshipping schedules. Calculating the throughputof the system must account for the combinationof single and dual cycles. 2021appropriate for various rate requirements and SKU counts.Many other variables also must be considered whenselecting technology for picking cases, boxes or totes.These include the footprint available for the system,building height, type of product and SKU velocity profile.As in a manual picking operation, the lines per order arevital when sizing a case level picking system. Lines perorder equates to the cycles the automation must conductper order. Some manufacturers use sophisticated pickingalgorithms that can optimize the motions the equipmentmust complete. For example, some software can createwaves of similar orders that require the same SKU.This reduces the number of cycles the equipment mustperform; however, the effectiveness of these algorithmsis affected by many factors, including order picking andPAGE 4 OF 5