A decision-support system for the design and management of warehousing systems

Computers in Industry xxx (2013) xxx–xxx

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COMIND-2510; No. of Pages 12

A decision-support system for the design and management ofwarehousing systems

Riccardo Accorsi, Riccardo Manzini *, Fausto Maranesi

Department of Industrial Engineering (DIN), ALMA MATER STUDIORUM - University of Bologna, Viale Risorgimento 2, Bologna, Italy

A R T I C L E I N F O

Article history:

Received 24 May 2013

Received in revised form 15 July 2013

Accepted 29 August 2013

Available online xxx

Keywords:

Logistics

Warehousing systems

Industrial storage systems

Decision-support system

Order picking

Material handling

A B S T R A C T

The issue of material handling involves the design and operative control of warehousing systems (i.e.,

distribution centres), which allow matching vendors and demands, smoothing with seasonality,

consolidating products and arranging distribution activities. Warehousing systems play a crucial role in

providing efficiency and customer satisfaction. The warehouse design entails a wide set of decisions,

which involve layout constraints and operative issues that seriously affect the performances and the

overall logistics costs.

This study presents an original decision-support system (DSS) for the design, management, and

control of warehousing systems. Specifically, the proposed DSS implements a top-down methodology

that considers both strategic warehouse design and operative operations management. The DSS can

simulate the logistics and material handling performances of a warehousing system. Heuristic methods

and algorithms address several critical warehouse issues, such as the order picking process, which is

responsible for 55% of the overall costs in a distribution centre. The benefits due to the adoption of the

proposed decision-support system are summarised as a dashboard of key performance indicators (KPIs)

of space and time efficiency that allow logistics providers, practitioners, and managers as well as

academicians and educators to face real-world warehousing instances and to find useful guidelines for

material handling.

� 2013 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Computers in Industry

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1. Introduction and background

In recent years, enterprises have completely reconfigured theirsupply chain to address increasing customer service levels anddemand variability. Warehouses play a pivotal role in the supplychain, and requirements for warehousing operations have signifi-cantly increased. Specifically, the customer needs in terms of theorder accuracy and response time, order frequency, order quantityand order size have dramatically changed with the global economyand new demand trends (e.g., e-commerce). The literature haswidely debated the issues of warehouse design and management,which is aimed at minimising the operation costs and time andincreasing the supply chain performance. Comprehensive surveyson warehouse and industrial storage system topics have beenproposed by De Koster et al. [1], Gu et al. [2] and Dallari et al. [3].

The main function of the warehousing systems is to receiveproducts (from inbound or manufacturing lines), to store materialsuntil they are requested, and then, to extract products frominventory and ship them in response to the customers’ orders.

* Corresponding author. Tel.: +39 051 2090468; fax: +39 051 2090484.

E-mail address: [email protected] (R. Manzini).

Please cite this article in press as: R. Accorsi, et al., A decision-supportComput. Industry (2013), http://dx.doi.org/10.1016/j.compind.2013.

0166-3615/$ – see front matter � 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.compind.2013.08.007

Fig. 1 illustrates a conceptual framework for classifyingwarehouse operations, considering the definitions of entities,processes, activities, and decisions as related to storage systems.

Products typically arrive in large units, such as unit-loads, andstandard or custom containers, or pallets, which cause the relatedlabour and handling activities to be less expensive. Incomingproducts must be put away, which is the most significantwarehouse function. The put-away process entails a set ofinterdependent decisions [2]: given a warehouse configuration(based on the layout parameters of Fig. 1), how much inventoryshould be held for a generic SKU (the so-called allocation in Fig. 1),and where should it be stored (the so-called assignment in Fig. 1)?

The warehousing system pursues the transformation of thelarge and relatively homogeneous arrival materials into small,frequent and heterogeneous output quantities in response tocustomer demands. The small and frequent output quantitiesresult from the fulfilment of the customer order lists.

Order picking is one of the prime components of labour andwarehouse-associated costs. Two alternative configurations oflayout types are common for picking. One, the so-called multi-levelpicking (see Fig. 1), executes high-level picking directly fromstorage locations, which are all accessible by picking equipment(e.g., turret-trucks). The other, the so-called forward-reserve

system for the design and management of warehousing systems,08.007


mailto:[email protected]


http://www.sciencedirect.com/science/journal/01663615


Fig. 1. Framework for warehouse design and operation issues.

R. Accorsi et al. / Computers in Industry xxx (2013) xxx–xxx2

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(see Fig. 1), executes low-level picking from the easily accessibleforward area, which holds the bulk of the inventory for everyproduct in a larger, reserve storage area. When given a product forwhich the inventory is low in the forward area, replenishment isrealised from the reserve. For an exhaustive description of thepatterns that are depicted in the proposed framework, a definitionof zoning, batching and routing in warehousing is given. Thezoning comprises partitioning the warehouse into different zones,which correspond to work stations. Pickers are assigned to zones,and workers progressively assemble each order, passing it alongfrom zone to zone. The batching comprises making a pickerretrieve multiple orders in one trip. Even though batchingrepresents a very useful approach to reduce travelling, it requiresthe retrieved SKUs be sorted into a single order. Lastly, the routingdefines an appropriate sequence of items on the order list to ensurea good route through the warehouse.

Overall, two main aspects lead to enhanced performance: thewarehouse design (1) and the operations control (2).

The first aspect refers to the layout constraints and parameters(illustrated in Fig. 1), the storage equipment and the high-levelstrategic decisions on the total inventory of the facility. The secondaddresses the warehouse operative activities, such as put-away,replenishment and order picking, focusing on models, techniques,and methodologies to enhance the operative performances (e.g.,zoning, batching, routing). These two aspects significantly affectwarehouse performances and have a direct influence on the level ofservice of the overall logistic chain (i.e., the steps before and afterthe warehousing system of Fig. 1).

The literature proposes a wide set of warehouse KPIs thatinclude the throughput capacity (the material flow processedthrough the warehouse per time unit), the storage capacity, theresponse time (the time within the order arrival and its shipment),the cost rate, and the cost per unit of material flow shipped by thewarehouse. All of these metrics are affected by the management ofspace and time, which are critical for every logistic process.

Generally, the contributions of the literature address theproblem of warehouse design rather than the management ofwarehouse operations separately. Gu et al. [2] describe inbound/outbound processes and review the literature, classifying thepapers on the basis of the scope of analysis, the adopted methodand the type of the observed warehouse (e.g., automated,conventional multi-aisle storage systems).


Typically, warehousing problems are non-polynomial (NP)problems and have a very large amount of real-world data tomanage. Therefore, user-friendly and timeless solutions for thewarehousing issues are ambitious aims for computer-basedapplications.

The remainder of this study describes the conceptual designand development of a decision-support system (DSS) for thestrategic design and the management of operative activities in awarehousing system. Specifically, it supports the design ofcomplex multi-zone forward-reserve picker-to-part storagesystems and provides multi-scenario simulation for KPI assess-ments. The DSS implements sets of heuristic methodologies tosupport data-oriented analyses and performance enhancement.

The management and control of warehousing system (i.e.,industrial storage system) activities and processes range amongvarious design alternatives and involve different expertise. Forexample, the problem of layout design, the definition of the totalstorage capacity, the determination of the number of aisles, thetypes of racks, the locations of the products (i.e., stock-keeping-units or SKUs) within the storage area, the stock per each SKU,and so on, involve interrelated areas and are challenging but canbe addressed through a unique modelling formulation. Themajority of the contributions reviewed in the literature [1–3]focus on a single aspect of the warehousing problem, therebyignoring the integration of multi-purpose approaches.

The proposed DSS develops a top-down methodology for thecomprehensive design of a warehousing system that allowsfor the decision-maker to develop and compare differentconfigurations and scenarios in a user-friendly computerenvironment. It implements multi-scenario simulation techni-ques to address real-world case studies, to highlight theinterdependency among decisions and to identify useful guide-lines about warehousing issues.

DSSs are computer-based tools that have been adapted tosupport and aid complex decision-making and problemsolving [4,5]. Research in this area typically highlights theimportance of information technology in improving efficiencyadopted by users to make decisions, improving their effectiveness[6,7]. Specifically, the literature reveals the benefits of usingcomputer-based systems to support logistics management,especially in the areas of logistics, transportation, and warehousing[8–10].



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Rouwenhorst et al. [11] and Svestka [12] develop interactivedecision support systems that are aimed at the conceptual designof dedicated storage systems to store and retrieve pallet loads (i.e.,unit loads). Other studies present tools for managing order pickingsystems (OPS) (i.e., less-than-unit loads), which support theanalysis of operating data (e.g., the SKU master file, order masterfile, inventory master file) to determine the requirements for theOP operations and storage capacity [13,14].

Currently, the literature does not provide any contributions thatcan combine warehouse design and operations patterns into aunique analysis, as suggested in the proposed DSS.

The proposed DSS is written in a high-level programminglanguage (C#) that utilises a relational database that can gather,store and manage datasets from a real-world warehousinginstance. Warehousing systems generally collect tens or hundredsof thousands of SKUs, with customer demands of millions of orderlines per year, while managing inbound-outbound processes,quality checking, and scheduling shipments. For this purpose,industry invests in the development of integrated informationsolutions, which are referred to as warehouse managementsystems (WMS). These commercial systems provide a real-timeview of material handling, often advising the efficient use of space,labour, and equipment [15]. Nevertheless, WMS solutions com-prise management systems that have no functionalities that arerelated to decision-making on warehouse design and optimisation.

The lack of systemic methodology on this topic highlights theneed to provide a DSS that can gather data from real-worldinstances and implement sets of effective heuristics to rapidlysupport decision processes on warehousing design and manage-ment. The aim of this study is to illustrate an innovativearchitecture of DSS for the analysis of warehousing systems whileconsidering the layout features, storage equipment, allocation andassignment problems, adopting numerical simulations to assessresults, statistics and performances.

The expected results of the proposed computer aided systemcan be exploited by disseminating knowledge among logisticproviders, practitioners, and managers, by educating and improv-ing industrial engineer expertise and by analysing real-world casestudies.

The remainder of this study is organised as follows. Section 2presents a design-support methodology for warehousing sys-tems and reports a more relevant definition of the main leverageof analysis. Section 3 illustrates the developed DSS functionali-ties through graphic user interfaces (GUIs) and the data-management section. Section 4 gives a picture of the potentialresults and analyses that were conducted through the applica-tion of proposed DSSs to real-world industry instances. Lastly,Section 5 discusses the conclusions and provides directions forfurther research.

2. Solving warehouse design and management issues: a top-down procedure

The proposed DSS implements a top-down procedure for thedesign and management of a forward-reserve OPS, as illustrated byAccorsi et al. [16]. This methodology organises procedures, models,and algorithms in an organic sequential decision to provide a wideset of solutions for storage layout, storage allocation, and storageassignment. The decision-maker conducts a sequence of analyses,generating sets of alternative warehouse configurations to beassessed through a what-if multi-scenario simulation. The goalperformance is the minimisation of the total travelled distance dueto picking, which represents 55% of the overall warehouse costs.The reduction in the distance means a reduction in the number oftravelling material handling solutions (e.g., forklifts and guidedvehicles) that are necessary to move materials, a reduction in the


vehicle congestions, parking areas, costs of travelling, mainte-nance, labour, and other outcomes.

Thus, the feedback flow illustrated in Fig. 2 allows for the user torearrange his/her decisions to achieve efficiency in both thewarehouse design and the operations. The following sub-sectionsfocus on the main decision steps.

2.1. Layout

The first decision involves the design of the warehouse layout.The study of a warehouse layout is based on the assessment of thefacility storage capacity. The proposed methodology is based onthe historical inventory and customer demand (or the demandforecasts) that are assumed as input for a stock out risk evaluationanalysis that is aimed to establish the required storage capacity ofthe warehousing system (i.e., designed from a green-field).

The purpose of this step is to set the facility layout through thedefinition of a set of parameters (see Fig. 1), such as the shapefactor, the number of aisles, the number of bays per aisle, the racksizes and types, and the characteristics of the unit load (i.e., thepallet size or other container solutions). The DSS rationalises thewhole storage space by devoting different zones for different SKUsin terms of the shape and size, which likely require specific racks orequipment.

2.2. Allocation

The storage allocation strategies establish a fraction of theoverall available storage space in the forward area to be devoted tothe generic SKU according to specific criteria, given a certain timehorizon. An equal space (EQS) strategy devotes the same fraction ofspace to each SKU, while an equal time strategy (EQT) ensures thesame number of restocks for each SKU given a selected timehorizon. Both of these strategies are renowned in industry and aresuitable for every storage context (i.e., cartons-cases picking). Theoptimal strategy (OPT), as proposed by Bartholdi and Hackman[17] and previously by Hackman and Rosenblatt [18], minimisesthe restocking to the forward area for pieces-picking (i.e., the orderpicking for the small parts). The proposed DSS implementsdifferent so-called allocation strategies by which to configurealternative scenarios of stock for every SKU. In the forward area(i.e., the fast-pick area or the low-level locations), the choice of thestock level to devote to each SKU affects the replenishmentactivities as well as the picking processes because this choiceinfluences the locations of the SKUs [16]. The system also supportsa pattern [17] to determine the sub-set of SKUs that maximise thenet-benefit of the forward area, considering both the time savingsper pick (i.e., the pick from the forward vs. the pick from thereserve) and the time for replenishment.

At this step, the decision-maker matches the allocation resultswith layout features and eventually considers the opportunity toreturn to the top for re-layout planning.

2.3. Assignment

The storage assignment strategies establish the appropriatelocations to assign to the SKUs in accordance with differentheuristics. The DSS asks the behaviour of selected SKUs within thedemand profile for a selected time horizon. Information on thepicking processes is collected to compute a panel of metrics usedfor SKU classification. Specifically, an index-based assignmentpolicy classifies the overall set of SKUs according to the effectivecriteria as the popularity (P) (i.e., number of requests per eachSKU), the turn-over (T) (i.e., the ratio of the demand to theinventory for each SKU), the cube-per-order index (COI), the orderclosing (OC) (i.e., the ability of an item to close an order). For a



Fig. 2. DSS top-down decisional methodology.


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detailed description of such heuristics and methods for index-based assignment policies, a recent literature contribution [16] isrecommended.

Another relevant aspect that can be considered through theproposed DSS is the correlation among the SKUs that are requestedtogether by customers. Correlated-based assignment policies canbe applied to group SKUs that are requested together and assignthem to storage locations that are close to each other, to save on thetravelling needed for the picking activities. The implementedcorrelated-based approach comprises the following three mainsteps:

Correlation analysis. The level of correlation is generallymeasured by introducing a similarity index among the SKUs. Thisprocedure allows comparing general-purpose similarity indices,e.g., the Jaccard index proposed by McAuley [19] and certainproblem-oriented issues.

Clustering. This step concerns the adoption of hierarchicalclustering algorithms (e.g., single linkage, complete linkage, group


average) and different similarity-cut thresholds of a dendrogram(i.e., value-based, percentile-based) [20–22].

Cluster Assignment. This step computes the above-mentionedmetrics (i.e., popularity, turn-over, order closing) for each cluster ofSKUs (e.g., the popularity of a cluster is given by the weighted sumof the popularity of the included SKUs) and to sort the clusters ofSKUs accordingly, as summarised in Accorsi et al. [16].

Regardless of the adoption of specific assignment policies (i.e.,index-based or correlated-based), the assignment step returns alist of SKUs (or a cluster of SKUs) that are sorted in accordance withthe selected criteria, to be properly matched with a list of locations,ranked by the increasing value of the single-command (SC) path(i.e., the distance to visit a location from/to a shipping/receivingdock). The computation of the SC depends on the location of theshipping and receiving docks (e.g., left/right corner, distributed,same side, different side) and on the so-called aisle-visitingstrategies (i.e., mono- and bi-directional aisles). To fit the layoutconstraints multiple (i.e., approximately twenty), configurations of




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both aspects are implemented. Lastly, each SKU is assigned to themost convenient available location in accordance with a greedyheuristic approach.

2.4. Multi-scenario simulation

The set of decisions that were previously addressed by the DSS(i.e., the layout design, allocation and assignment) provides aspecific configuration for a warehouse scenario. Multiple iterationsof the DSS allow for generating multiple warehouse scenarios,which differ in their layout configuration, storage allocation, and/or storage assignment criteria. In conclusion, a what-if multi-scenario simulation of operative performances (i.e., travelling forput-away, replenishment and picking) enables the decision-makerto assess the best solution for the warehouse design andmanagement by the minimisation of the total travelling distance,time and cost.

3. DSS functionality and design

The DSS provides a useful and user-friendly tool for managersand decision-makers who have no background and expertise inprogramming and software development but who frequently facewarehousing system design and operations issues. The DSSimplements database management system (DBMS) architecturesfor data storage, models and heuristic algorithms and user-friendlygraphical user interfaces (GUI) that enable interactive queries,reporting and graphic visualisation.

The proposed application is based on a stand-alone database.Decision-process inputs with regard to operative features, costs,and other parameters are generally handled by practitioners inwarehouse operations, whilst outputs comprise operative KPIs thatare usually tracked in the real world (e.g., the pick-rate, time/travelling for picking). The SQL database architecture enables usersto gather, store and manage a very large amount of data quickly,which is gatherable by users through dynamic queries. Further-more, graphical 3D views of warehousing scenarios are drawnautomatically by an ad-hoc graphical user interface with Auto-CAD1.

The application is organised around a main GUI that presents allof the principle features and commands to load data or projectsand to save results. The tool enables the following mainfunctionalities:

� Design a new warehousing system (we call green-field).� Import the existing layout (we call brown-field) to perform an

allocation-assignment analysis.� Run the DSS for a complete layout-allocation-assignment

analysis of a generic warehouse zone (i.e., in accordance withwarehouse zoning).� Merge single-multiple warehouse zones (i.e., in accordance with

warehouse zoning) as an aggregated system.� Implement heuristics for storage allocation, assignment, single-

order picker-routing, order-batching.� Develop a what-if multi-scenario analysis for the warehousing

KPIs.� Draw a graphical 2D/3D warehouse in agreement with different

designed scenarios.

3.1. Database considerations

The DSS utilises the aggregated historical data that is stored inthe database as the foundation for the application of all of theheuristics, methods, and analyses. This section focuses on theinformation and data architecture as a basis of the proposed DSS. Inwarehouse operations, the very large amount of data that is to be


handled is critical. Warehousing systems manage tens ofthousands of SKUs that are picked from thousands of locationsto fulfil thousands of demand lines per day (see, for example, spareparts storage and management systems in the automotiveindustry). Warehousing activities are usually tracked by enterpriseWMSs. The preliminary step of analysis comprises filtering theavailable historical information (e.g., the SKU master file,inventory, demand) to build a comprehensive stand-alone data-base in accordance with the entity-relationship (E-R) diagramillustrated in Fig. 3.

The developed DBMS represents an interface between the dataand the decision-maker. This system involves the processing of aconsiderable amount of data (see Fig. 4), which is necessary todescribe univocally the characteristics of the warehousingsystem.

This system comprises a relational SQL architecture that ispowered by AccessTM but is quickly replaceable by any othercommercial DBMS (e.g., MySQL1, DB21). The databaseincludes a set of tables (see Table 1) that allows for acomprehensive description of the system’s object of analysisthrough a typical snowflake structure. Meaningful preliminarystudies on the unified modelling language (UML) and E-Rdiagrams are crucial to designing an informative architecturewith the tool and to aid in further code maintenance of themodifications [23,24].

This database architecture has various advantages. First, itenables users to track the inventory and to localise a generic SKU inboth the forward and reserve storage areas. On the other hand, itallows for a wide set of dynamic views and queries to create aperformance dashboard of the warehousing system.

The client side comprises a user-friendly interface made byGUIs. The decision-maker plays opportunities to design awarehouse zone from ground-zero (i.e., a green-field scenario),to add a new zone to an existing warehouse (called here an‘‘expansion scenario’’) or to import an existing storage zone for anallocation-assignment analysis (i.e., a brown-field scenario).

A what-if multi-scenario simulation of put-away, replenish-ment and order-picking and outbound (e.g., picking) activities isperformed as a benchmark to assess the efficacy of each scenarioand the effectiveness of the adopted allocation and assignmentpolicies. To enhance the picking performances, an order-batching algorithm (i.e., whose description is not in the scopeof this paper) based on a clustering approach is implemented asa batching tool, and a travelling salesman problem (TSP)heuristic (i.e., nearest neighbour) is developed as a routing tool.

3.2. Graphical user interfaces (GUIs)

GUIs enable the user to conduct analysis and to leaddecisions through the DSS. The main window presents a toolbarto load or save a project. During any execution run, the userspecifies the domain and dataset object of analysis. Statisticsand results are summarised on the bottom of the control panelas a quick report window to inform the decision-maker aboutthe computer processing. For each project, multiple ware-housing scenarios can be developed. A what-if experimentalanalysis based on a dynamic simulation can be conducted tocompare the performance of the warehousing system underdifferent configurations and operating conditions. At the end ofeach simulation, the obtained results and KPIs are depicted andstored into the appropriate tables of the database (see Table 1).One of the advantages of saving the results of each run is theopportunity to draw out effective guidelines for the design andmanagement of complex warehousing systems. The GUIcomprises distinct modules that are further detailed in thefollowing sub-sections.



INVENTORY

PK,FK1 ItemCodePK Period

CartonStockFWCartonStockRSULStockFWULStockRS

ORDERLIST

PK PeriodPK OrderCodePK,FK1 ItemCode

PkdQtyPkdVolumePkdWeight

SKU

PK ItemCode

PeriodDescriptionCategoryCartonLengthCartonWidthCartonHeightCartonVolumeCartonWeightULCodeWeightPerVolumeCartonPerULPiecesPerUL

FK1 WHCodeWH

PK WHCode

WHTypeWHLengthWHDepthAisleBayAisleWidthCrossingAisleWidthBMLengthBMWidthBMHeightLayerPerBMULPerBMULPerBMLayerBMDepthRackLevel

FK1 ColumnCodeBeamCodeCrossingCodeLevelsFW

FK2 ULCodeRoutingDockInDockOut

RACK

PK RackCode

RackTypeLengthTolleranceBaseXBaseY

MB

PK MBCode

FK1 WHCodeCoordinateXCoordinateYCoordinateZMBLengthMBWidthMBHeight

LOC

PK LocCode

FK1 MBCodeItemCodeCarton

UL

PK ULCode

ULLengthULWidthULHeightULWeight

VEHICLE

PK VehicleCode

DescriptionVehicleTypeVehicleLengthVehicleWidthVehicleHeightCurveRadiusLoadWeightLoadULLoadVolumeSpeedHzSpeedVtAccelerationHzAccelerationVtLiftLimitWHCode

SIMULATION

PK Code

PeriodWHCodePeriodFromPeriodToBatchSimilarityIndexClusteringAlgThresholdPercentileThresholdValueVehicleCodeOrderList

OUTPUT

PK Code

PeriodOrderCodeTripItemCodePkdQtyDistanceHzDistanceVtDistanceHzDockOutDistanceTotalTimeDistanceRestock

SCENARIO

PK SimCode

WHCodeInvMngStrategyStorageCapacityShapeFactorAisleNumBayNumLocNumRackLevelPlantLengthPlantDepthPlantHeightULCodeLayerPerMBULPerLayerAisleWidthCrossingAisleWidthFWLevelAllocationStrategyAllocationFromAllocationToAssignmetIndexAssignmentFromAssignmentToSimilarityIndexPOI1POI2POI3POI4POI5ClusteringAlgThresholdPercentileThresholdValueClusterSortingRoutingStrategyDockInDockOut

Fig. 3. E-R diagram of the proposed DST.


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3.2.1. Layout GUI

To begin the design of the warehousing system, the decision-maker sets the total warehousing holding capacity. Given a dataseton the historical demand or inventory, the user must guarantee theoverall level of the stock and properly organise the available space.The leverage handled through the proposed DSS for theconfiguration of the layout are the shape factor, the unit loadsizes, the unit load location, the base module sizes, the aisle widthand number, and the rack types, as illustrated in Fig. 5.


This GUI reports the characteristics of the layout configuration,such as the total storage capacity, the numbers and sizes of theaisles and bays, the storage saturation (i.e., the ratio of the storagevolume to the overall available volume), the number of SKUsstored per each aisle or per each bay, etc.

Once the warehouse is designed (or imported), the DSScomputes the three-dimensional coordinates of all of the locationsand stores them into the database (see Fig. 3) for further simulationanalysis. The DSS even includes an AutoCAD1 application, which



WarehouseData Input

Order HistoryFile Inventory File Inbound

Activity FileLayout

Features FileSKU Master

File

Item CodeItem DescriptionCategoryPackage SizeSales PriceItem Turn Class

Customer CodeCustomer AddressDue DateItem OrderedOrdered Qty,Weight or Volume

Item CodeSnapshot DateStocks Qty per ItemStorage Area

Delivery CodeArrival DateDue DateCross-dockingItem CodeDelivered Qty,Weight or Volume

Storage Area CodeStorage EquipmentStorage SizeType of RackRack Sizes andPropertiesNum. of LocationsSize of Locations

Fig. 4. Data required to develop a DSS for warehousing issues.


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adopts real-world rack libraries to make a bi-dimensional andthree-dimensional picture of the warehousing system.

Specifically, this application allows for analysts and logisticsproviders to import rack components (e.g., beams, columns) thatare taken from manufactures’ catalogues and configure a truthfuland accurate warehouse layout. The system evaluates themaximum load weight of the inventory and checks for theavailability of appropriate rack components that are suitable intheir sizes and characteristics. Fig. 6a gives a picture of some three-dimensional views of warehouses that result from the DSSapplication. As a result, the detailed list of parts is given as arough estimation of the total investment.

3.2.2. Allocation GUI

This GUI allows for the user to compare different allocationstrategies that were attempted to allocate the appropriate storagevolume to a generic SKU within the forward area for a typicalforward-reserve picker-to-part OPS. The DSS encompasses fourmain allocation strategies, three of which were previouslydescribed in Section 2.2; one is hereby proposed, the so-called

Table 1DSS database tables.

Data

SKU Contains data regarding the SKU’s properties and cha

ORDERLIST Contains the order history file of a horizon of analysi

INVENTORY Includes the inventory file for every SKU for all of the

WH Involves properties and features of the layout and sto

number of levels, location sizes). Through such a tabl

according to allocation-assignment analysis

MB Includes the list of bays within the warehouse

UL Contains the properties and characteristics of the hol

LOC Reports the list of locations with details on the bay, l

RACK Describes the type and characteristics of the commer

SCENARIO Summarises the setting of the layout leverage, allocat

the decision-maker through the top-down analysis m

VEHICLE Includes the list of storage equipment (i.e., vehicles) a

SIMULATION Reports the list of simulations that were conducted b

to compare their performances or to different time ho

performances for a scenario)

OUTPUT Summarises the statistics of the simulation in terms

replenishment) and outbound (i.e., picking) activities


EQT*, which allocates the appropriate storage space to each SKUconsidering the demand in terms of the retrieved volume and picklines. This module has an open architecture that eventually allowsan easy implementation of other additional allocation strategies.

Fig. 7 illustrates the GUI such as is proposed to the decision-maker. On the left, two input command windows (‘‘Rack Level (n.)’’and ‘‘Allocation Strategy’’) are presented to define the number ofrack levels that are devoted to the forward area and to select theallocation strategies by which to adopt. Thus, the user has theopportunity to configure a low-level or high-level picking systemand assign the highest levels to the reserve storage area.

The calendar panel (on the left of Fig. 7) selects the horizon ofanalysis, by filtering the dataset through dynamic SQL queries.Different time batches are selected to compute the fraction of thestorage volume that is devoted to each SKU according to thehistorical demand and inventory data. For example, given atemporal batch (from August 31st, 2011 to September 28th, 2011),a panel of allocation strategies accordingly allocates to every SKUthe storage volume, cartons, and unit loads within the forward area(see the tables in the middle).

racteristics and generally accounts for ten thousand rows

s (e.g., a couple of years) and generally comprises millions of lines

storage areas. Multiple inventory snapshots report the stock trends

rage areas (e.g., the shape factor, rack size, number of aisles and bays,

e, the DSS imports an existing warehouse system to be evaluated

ding units and pallets in which the items are stored

evel, aisle, filling product and related quantity

cial rack uploaded into the database. The sizes and load tolerance are reported

ion and assignment policies and all of the parameters and choices selected by

ethodology. The results from each scenario by iteration are illustrated in Fig. 2

nd the related properties in terms of the operative performances

y the decision-maker. Multiple simulations might refer to different scenarios,

rizons adopted for the same scenario (i.e., to assess the trend in the

of the travelled distance and time for each line of inbound (i.e., put-away,



Fig. 5. Layout GUI.


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Storage space is often a precious resource to be handled, toreach efficiency and reduce operating costs. At this step, thedecision-maker can evaluate the net benefit of the forward area,according to the pattern that was briefly introduced in Section 2.2.The sub-set of SKUs, which maximises the net-benefit of theforward area, corresponds to the maximum value of the curvedepicted in Fig. 7.

If an existing warehouse zone/system is imported and loaded,the AS-IS inventory per each SKU (i.e., the number of cartons andunit loads in both the forward and reserve areas) is known, andrelated data are stored into the database. Thus, the user can skipthe allocation module, which is not considered as leverage of theanalysis, leaping from the layout design module directly to thestorage assignment problem.

3.2.3. Assignment GUI

This GUI leads the decision-maker towards the assignmentissue by the definition of the appropriate location to assign to ageneric SKU in the forward area. Considering the horizon ofanalysis (i.e., the same chosen for allocation analysis or different),the user classifies SKUs according to a set of proposed criteria ormetrics (i.e., the index-based functionality), to assess the correla-tion among the SKUs (i.e., correlation-based functionality) througha clustering approach.

Both of the opportunities compute a ranked list of the SKUs(eventually computing clusters of SKUs), respond to specificcriteria (see the previously cited popularity, turn, and orderclosing), to be properly matched with a list of locations,according to the procedure presented in Section 2.3. More thantwenty combinations for the sites of the shipping and receivingdocks (e.g., corner, middle, bottom-up) that affect the single-command time to access a generic location are considered.

Fig. 6. (a and b) Three-dimensional views


Once the appropriate location in the forward area is assigned toa specific SKU, the reserve area is accordingly arranged by theadoption of greedy heuristics to reduce the distance between anitem and its reserve.

The results of the assignment module are store into thedatabase and are roughly illustrated as the bird’s eye view of thedesigned warehouse zone. The bird’s eye view is a frame shot of theSKU locations, where each SKU is differently coloured, and thestorage details (e.g., the location code, item code, and number ofcartons per item) are summarised. The DSS also fills the rack withthe SKUs in the designed layout in a three-dimensional view (see asample in Fig. 6b). By considering real commercial racks, thedecision-maker obtains a ready-to-print version of the designedwarehouse that is useful for equipment and systems manufac-turers and providers as well as warehouse operators who areresponsible for put away and picking activities.

3.2.4. Simulation GUI

In warehousing operations, different categories of SKUs interms of the shape, volume, weight or size of packaging areassigned to different zones, adopting different types of rack orstorage equipment according to a zoning approach. Severalconfigurations for the storage zones, separately and independentlydesigned through previous GUIs, are hereby saved and are furtherselected by the decision-maker to be merged into a unique system(see Fig. 8a).

This GUI enables us to configure articulated and complexwarehouses that are made by different storage zones, ascommonly occurs in real-instance warehousing problems (exem-plified in the literature by [25]). Furthermore, this GUI matchesthe decisional steps with what-if simulation analysis. By settingthe layout (i.e., merging the warehouse made by one or multiple

of warehouses designed with the DSS.



Fig. 7. Allocation module of the DSS.


G Model


storage zones), the decision-maker imports the location coordi-nates for each zone and arranges them according to the overallwarehouse layout configuration.

The DSS calculates, for every location, the forward area andreserves the travel path (in terms of distances) from the shippingand receiving docks and those from/to each other (see Fig. 8b).

The what-if simulation analysis involves inbound (e.g., put-away, restocking) and outbound (e.g., order picking) operationsand provides a useful tool to assess system performances,including costs (i.e., in terms of the travelled distance and time)within a specific horizon of analysis. The DSS reports a completepanel of statistics and KPIs to evaluate the efficacy and efficiency ofthe layout, allocation and assignment configuration. A list ofstatistics includes the travelled distance (horizontal and vertical)and time due to pick-path, travelled distance (horizontal andvertical) due to put away and replenishment, time waste due tostock-out, number of replenishments per each SKU, number ofvisited aisles, as a metric of the vehicles congestions, spatial pick-density, and other aspects.

Fig. 8. (a and b) Simulati


4. Case study

In this section, the proposed DSS has been applied for the designand performance assessment of a real-world warehousing system. Inparticular, this case study addresses a spare parts managementsystem for an international brand of the automotive industry. Alogistic firm operating worldwide provides the logistics services oftransportation (inbound and outbound) and warehousing for animportant automotive company to supply the demand of spare partsto hundreds of Italian dealers. This system is a regional distributioncentre (RDC) that accounts approximately 8000 SKUs as spare parts,ranging from bonnets to screws. The high variability of SKUs in size,weight, and shape is typical for automotive industry, and requiresproper storage management practices. The analysed system accountstwenty-four aisles arranged in a multi-zones warehouse, which holdsfour storage zones grouping homogeneous SKUs in size and shape ofunit load and similar in weight. Each storage area presents a differenttype of racks, which is suitable to allocate a specific set of SKUs (e.g.,cantilever for door, bin shelving for air-filter, etc.).

on GUI: pre-setting.




G Model


The observed low-level single-order picker-to-part andforward-reserve OPS represents a relevant sample to assessthe effectiveness of the proposed tool. The receiving andshipping activities are decoupled and limited respectively tothe left and right of the dockside. Although the receiving andshipping docks are distributed along the warehouse side, thepicking process starts on the bottom left corner of the systemand the ends on right bottom corner. These two control pointsrepresent respectively the parking of walkie-stackers and roll-containers and the sorting/packing station for the orders to beshipped. The low-level storage area (i.e., the forward area) is25,000 square metre wide. Customer orders, made by manyorder lines, accounts on average 37 lines, results in long time-effective picking missions, since the pickers has to achieve insequence products which are far located one from the others.The presence of narrow aisles does not allow reverse back, andthe ‘‘traversal’’ visiting strategy is adopted either in real worldthan in the simulation.

The complexity of the system bases on the disomogeneity ofboth the SKUs and the processes. The increasing complexity ofmodern supply chain shifts the role of warehousing systems inaddressing demand variability, pushing logistic providers tohandle both homogeneous and heterogeneous flows. Such a trendresults for the observed warehouse in articulated inbound/outbound operations, which include the truck unloading, thecheck of loads, put-away and replenishment, and the order picking.A dashboard of KPIs involving put away, replenishment and orderpicking missions allows the decision-maker to address operativecriticalities and propose strategies for both layout re-design andoperations improvements.

Table 2The results of a multi-scenario analysis.


The DSS implements a what-if multi-scenario simulation tocompare how different allocation and assignment strategies affectthe performance of the inbound/outbound operations in theobserved warehousing system. The simulation analysis does notinvolve layout leverage, which is common to any proposedscenarios, since the client had no budget for layout re-design orinfrastructure refurbishing. For sake of brevity, the comparativeanalysis regards just with the reduction of travel distances, as anapproximation of the operative time.

Table 2 illustrates the results of a simulation campaignconducted on the historical set of inbound/outbound annualoperations, which account about 970,100 picking lines, and 25,500replenishment missions. The colours yellow, red, and blue refersrespectively to the layout, allocation and assignment steps ofanalysis implemented into the DSS (see Fig. 2).

The overall warehousing system is composed by the fourstorage areas, which are independently designed in accordancewith the allocation and assignment policies. In particular, thewarehouse scenarios are organised through the adoption of thefollowing rules and parameters:

� 3 allocation strategies (i.e., EQS, EQT, OPT).� 4 assignment strategies (i.e., popularity, COI, turn, OC).� 1 visiting strategy (i.e., traversal).� 1 routing heuristic strategy (i.e., nearest neighbour).

Different allocation strategies result, first, in different values oftotal replenishments within the observed horizon of time, andsecond, if combined with different assignment strategies, ininfluencing the location of the SKUs in the forward area, thereby



Table 3Some tips from DSS implementations on real case studies.

Profile Case 1 Case 2 Case 3

Client business Grocery/catering Automotive Heavy machinery

Product category Food/drinks Spare parts Spare parts

Client role Warehouse owner 3PL Warehouse owner

Complexity

Picking approach Carton-picking-by-pallet Carton-picking-by-pallet Carton-picking-by-pallet

Warehouse system Forward-reserve Forward-reserve High-level forward

# SKUs 1667 7386 3235

# Storage area 3 4 1

Storage area (m2) 9000 25,000 5500

Observed period (months) 12 12 6

Picking (lines/period) 19,953 970,147 37,000

Replenishment (lines/period) – 25,426 –

Put-away (lines/period) – 44,729 –

Purpose

Allocation Allocation Allocation

Assignment Assignment Assignment

Results

Simulated period (months) 6 12 4

Simulated process Picking Picking; Replenishment; Picking

Best Scenario EQS; Correlated & P EQS; P EQS; COI

Travelling savings (D%) �18.11% �16.73% �22.57%


G Model


affecting the total travelled distance for all inbound/outboundoperations. The illustration of the alternative warehouse scenarioallows the decision-maker to recognise the influence of decisionson SKU allocations and assignments in both the forward andreserve storage areas. The saving of replenishment missionsoccurred by EQT and OPT strategies, are not enough to justify theirimplementation considering the overall costs. Indeed, the combi-nation of an EQS strategy and popularity rule accomplishesreducing the total travelled distance primarily because of thepicking activities.

This section gives a picture of the potential multi-leverageanalyses conducted through the proposed DSS. Significant time-savings can be generated by a re-allocation and re-assignment ofSKUs within the forward area of a multiple zones warehousingsystem. In the following section, the potential applications of theproposed DSS will be described with the focus on the enhancementopportunities in tackling real world instance and both strategicand operative warehousing decisions.

5. Discussion

Despite of the increasing trend of lean paradigm in productionand distribution operations, warehousing systems are still neces-sary to address the demand variability and seasonality, to matchvendors and consumers in global trade, to hold products andsustain the customer service level. The reduction in demandedquantity joined by the customization of items, results in raising thecomplexity of the warehousing operations, which are called toachieve high performances and to make goods travelling fastthroughout the distribution pipeline.

The proposed DSS supports the decision-maker in addressingwarehouse operations, which are highly dependent by a broad setof factors including the layout, the storage equipment andinfrastructure, the set of SKUs,the order profile, the SKUs turnover,the routing policies for put away and picking missions, the goalperformances in terms of time efficiency, space efficiency or both.

The implementation of this tool for real-world instance hasdifferent purposes. First, it supports the decision-maker inhandling long-term strategic decisions, based on the estimationof requirements of space and investments (i.e., costs for racks andstorage equipments) necessary to arrange a new storage area from


green-field. Second, it addresses mid-term tactical decisions,involving the definition of the storage areas devoted to pickingrather than bulk storage (i.e., forward-reverse low-level system vs.high-level system), the size and shape of each storage area, and theopportunity to set multiple storage areas dedicated to differentclasses of SKUs. Tactical decisions include also the analysis of theappropriate storage quantity to allocate to each SKU, therebyaffecting the reorder quantity from the distribution nodes atprevious stage of the supply chain. Third, the DSS handlesoperative short-term decisions, based on the assignment strategiesof SKUs to locations, the definition of the best performing routingpolicy, the selection of the retrieving strategy to adopt (i.e., single-order vs. order-batching). The what-if multi-scenario simulationanalysis assesses the operative performances of each scenario,providing improvements solutions and enhancement guidelineswith operative, tactical and strategic horizons of analysis.

Based on the described functionalities, it provides various levels ofassistance to different users. Specifically, the DSS supports the thirdpart logistic (3PL) managers in facing daily concerns on themanagement of multiple-client storage systems, characterised byhigh-variability in items, storage racks, and turnovers. The tooldepicts a detailed dashboard of the operative performances of ageneric storage area (i.e., a generic client), with suggestion for tacticaland operational improvements and tips for scheduling labour amongdifferent areas. Furthermore, the DSS offers to warehouse owners theopportunity to simulate the operative savings (i.e., time, costs, andspace) achieved by the combination of allocation and assignmentstrategies, which results in changing approaches for the managementof SKUs slotting. Finally, the DSS allows researchers approachingdifferent real case studies, testing the effectiveness of models andheuristics on providing performing solutions and creating knowledgeover the most critical and recurrent storage issues.

Table 3 reports the obtained results by the implementation ofthe DDS with three real case studies. These profiles were selectedas the basis for system validation since they were mostrepresentative for computational complexity and robustness ofenterprise datasets. Systemic analysis of the three profilesidentifies major opportunities for improvement over the AS-ISscenario. The three profiles differ for the industrial sector ofapplication, the set of SKUs, the purpose of the client and therelated implemented analyses.




G Model


Despite of the observed business, the DSS tends to support thegrouping of the fast-moving SKUs, grouped per affinity (i.e., Case1), per popularity or cube-per-order index (i.e., Case 2 and Case 3),within the most convenient storage areas, thereby eliciting theneed of dedicated golden areas, potentially supported by differenttypes of technologies (i.e., conveyor, semi-automated storage/retrieving systems). The tool properly arranges the availablestorage space for both forward-reserve and high-level storagesystems, involving the design of different storage areas, whichincreases the pick density and space efficiency of the system.

6. Conclusions

An original decision-support system for picker-to-part storagesystem design and operations management is illustrated. Theproposed DSS comprises a user-friendly tool for supportingpractitioners, managers, decision-makers, and logistics providersby addressing real case studies and experimental analyses over thedesign and operations control of the storage systems. This toolenables us to gather and store information from enterprise WMSsand to elaborate, through an efficient DBMS architecture, a set ofdata-oriented design solutions and configurations. The tool aims todesign multi-zone storage systems and implements a wide panel ofalgorithms and methods that address different stages of analysis(e.g., storage allocation, assignment, batching, zoning, routing).Results and statistics on performances and costs due to a genericwarehouse scenario are computed through a what-if simulationanalysis. An implemented graphic interface draws two-dimen-sional and three-dimensional views of the designed storagescenario, adopting real commercial rack components with thepurpose of providing a ready-to-print release of the warehouse forlogistic providers and engineers.

Further developments are expected on the implementation ofinnovative methods, models and algorithms, to address warehouselayout, storage allocation and storage assignment issues in thepresence of automated storage solutions and equipment for part-to-picker systems (e.g., automated storage and retrieval systems(AS/RS), mini-load, carousels).

A useful module, integrating a cam interface for barcodereading, could be implemented to support the introduction andregistration of new SKUs and the updating of the enterprise SKUmaster file. This functionality might respond to the problem ofperiodical and partial storage rearrangement rather than overallwarehouse redesigning.

The educational purpose of this work is to provide a set of flexibleinteractive instruments to create and disseminate knowledgeamong logistic providers, practitioners, and managers, and toimprove industrial engineers’ backgrounds and expertise over themost critical storage issues. Lastly, the designed tool, similar toany other computer-aided system, attempts to support, but notreplace, the decision-maker, who responds daily to strategic designand operations management within a storage system.

References

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[25] R. Manzini, R. Accorsi, A. Regattieri, Order picking systems in spare parts man-agement. A case study from automotive industry, in: Proceedings of 17th Inter-national Working Seminar on production Economics, Innsbruck (Austria),February 20–24, vol. 3, (2012), pp. 13–24.

Riccardo Manzini is Associate Professor of ‘‘Logistics and Operations’’, ‘‘Reliability

& Maintenance’’ in the Department of Industrial Engineering at the University of

Bologna (ALMA MATER STUDIORUM), Italy. His academic research principally deals

with planning, design and control of production systems with particular reference

to logistics and operations, optimisation and decision support systems, reliability

modelling and maintenance. He is the author of about 130 published papers on

production systems, logistics and reliability. Director of the ‘‘Warehousing Center’’

and the ‘‘Food Supply Chain Center’’ at Bologna University. Author and Editor for

Springer of the book ‘‘Warehousing in the Global Supply Chain. Advanced models,

tools and applications for storage systems’’ (2012). Editor of the Special Issue

‘‘Decision models for the design, optimisation and management of warehousing

and material handling systems’’ (IN PRESS 2013) for the International Journal of

Production Economics, Elsevier. He has carried out several research projects in

cooperation with – and funded by – private and public companies on logistics,

industrial plants and maintenance problems.

Riccardo Accorsi is post-doctoral researcher at Department of Industrial

Engineering of the University of Bologna, Italy. In 2013, he defended his Ph.D. in

Mechatronics and Industrial Systems at the University of Padua, Italy. He received a

Master degree in Management Engineering at the University of Bologna, Italy, in

2009. His main fields or research are modelling and simulation applied to industry

and supply chain context, with particular focus on warehousing systems and

distribution networks enabling product lifecycle management.

Fausto Maranesi is a researcher at Department of Industrial Engineering of the

University of Bologna, Italy. In 2011 and 2009, he received respectively a Master

degree in Management Engineering and a Bachelor degree in Informatics

Engineering both at the University of Bologna, Italy. His current research interests

include warehousing modelling and simulation and computer applications for

manufacturing and logistics issues.


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A decision-support system for the design and management of warehousing systems

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