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Int. J. Production Economics 106 (2007) 82–91

www.elsevier.com/locate/ijpe

Developing a methodology to analyse despatch bay performance

Andrew Pottera,�, Chandra Lalwanib

aLogistics Systems Dynamics Group, Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive,

Cardiff CF10 3EU, Wales, UKbLogistics Institute, Business School, The University of Hull, Cottingham Road, Hull HU6 7RX, UK

Available online 9 June 2006

Abstract

As companies look to integrate their internal and external operations more fully, the role of the despatch bay is

increasingly being recognised as a key interface with transport operations. However, it can also be a source of delays in the

flow of materials, incurring additional costs to the company while reducing customer service levels. The aim of this paper is

to develop a robust methodology that identifies the causes of delays within the despatch process, provides

recommendations for improvements and enables these to be tested before implementation. As well as developing the

methodology, the paper demonstrates its application to a manufacturer of steel products. By using this methodology, it

should be possible to improve delivery performance and reduce transport costs by minimising the waiting times for

vehicles.

r 2006 Elsevier B.V. All rights reserved.

Keywords: Despatch bay; Process mapping; Simulation; Steel industry

1. Introduction

Despatch bays form a critical interface betweenthe internal processes of a company and theirtransport operations. Costs incurred in loadingvehicles and inefficiencies in this area directly affectboth customer service levels and the profitability ofa company (Morris, 1991). Attempts to improvedespatch bays are often limited to automation andnot process improvement. Increasingly, there isrecognition that significant gains can be madethrough the integration of transport (Stank andGoldsby, 2000), with the despatch bay being anintegral part of this process. With the Working

e front matter r 2006 Elsevier B.V. All rights reserved

e.2006.04.010

ng author. Tel.: +44 29 2087 6083;

7 4301.

ss: potterat@cardiff.ac.uk (A. Potter).

Time Directive (WTD) being enforced withinEurope for road haulage in 2005, there is anincreasing pressure from hauliers to keep vehiclesmoving as much as possible.

This paper investigates the despatch process for amanufacturer of steel products, where it had beenrecognised by managers that delays were occurring.While the issues raised are not unique to the sector,the experience of the authors suggests that thefindings are consistent with those for other steelcompanies. The aim of the paper is to develop arobust methodology to identify the causes of delayswithin the despatch process, and to recommend andtest potential changes.

The case study company is a manufacturer ofsteel products based in South Wales, convertingcoils into tubes. These are then distributed usingthird-party haulage by road to steel stockholders

.

ARTICLE IN PRESSA. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–91 83

predominantly within the UK. The company hassuccessfully implemented lean thinking (Womackand Jones, 1996) within its manufacturing opera-tions, but has not extended it to the despatch bayarea. Consequently, delays in loading and despatch-ing vehicles were leading to a fall in customer servicelevels. Further, overtime was needed to enable alldespatches to be loaded for delivery. One of theaims of the research was to eliminate this overtimeworking.

The paper proceeds by briefly reviewing some ofthe published work evaluating despatch bays andtheir performance. The methodology developed forthe research in this paper is then outlined in detailbefore being applied to the case study company.Conclusions are then drawn.

2. Despatch bays in supply chains

In most supply chains, there is a need to transportgoods from the point of production to the point ofconsumption. This provides both time and placeutility for the products (Coyle et al., 2003). Thedespatch bay (alternatively known as a loading bay)lies at the interface between the manufacturing/storage process and transport, and is a key part ofthe flow of products to the customer (Thompson,2000). Its importance is growing as time compres-sion becomes a common element of supply chainstrategies (Towill, 1997), requiring accurate andtimely deliveries. The key to effective despatch bayoperation is trading off an acceptable loading timewith cost, product protection and safety factors(Cork, 2005).

One issue is that it is not always clear who isresponsible for the operation of despatch bays,because they lie on an interface rather than within afunctional area (Parry, 1995). Consequently, delaysand inefficiencies can occur, affecting the ability ofthe supply chain to deliver time utility. This has a

Transp

Despatch Bay

Madeon time

Available inwarehouse

Delayedepartu

Delay

StorageProduction

Fig. 1. The despatch bay

significant impact on the customer service level andprofitability of the company (Morris, 1991). Fig. 1depicts the processes in a simple supply chain andhighlights the position of the despatch bay within it.While the products may be manufactured andavailable in the warehouse on time, a delay withinthe despatch bay process can lead to a decline incustomer service levels (Cork, 2005). In the UKgrocery sector, it has been found that problems atthe collection point occurred on approximately2.5% of all journeys (from a sample size of over15,000), with an average duration of 43min(McKinnon and Ge, 2004).

A further pressure on the despatch bay arisesfrom the introduction of the EU’s WTD in the roadhaulage industry from April 2005. This limits theworking week of a driver to an average of 48 h. InUK, drivers currently work 60 h per week on anaverage (Boughton, 2003). Therefore, the WTD willcreate a shortage of drivers to complete all the loadsdemanded by industry. To counter this, operatorswill be looking to maximise the time vehicles spendmoving products by reducing loading and unload-ing times.

In terms of literature relating to methodologiesfor despatch bay improvement, there is a need tofocus more broadly on the area of warehouselayout-planning techniques (for a review, seeDawood and Marasini, 2001). The efficient locationof stock within warehouses facilitates improvementsin the physical movement of products through theloading bay. Approaches used within this contextinclude operations research techniques, artificialintelligence and simulation. Dawood and Marasini(2001) developed a discrete event simulation (DES)model with the aim of improving the layoutof a stockyard for concrete products. One ofthe key measures was the time vehicles spent atthe yard waiting for and being loaded. The modelconsiders both the information and physical flows.

CustomerGoodsInwardsort

dre

Latearrival

Poor servicelevel

in a supply chain.

ARTICLE IN PRESSA. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–9184

Improvements were brainstormed in conjunctionwith the managers of the stockyard, and then testedon the simulation model.

The integration of warehousing and transportfunctions should also enable improved performancefrom the despatch bays, as these two functions meetthrough them. Mason et al. (2003) considered theintegration of warehouse and transport manage-ment systems, developing a DES model to test thebenefits. They found significant gains are possible inboth cost and despatch bay efficiency as a result ofintegration. In addition, DES models have beenused to test potential improvements on the transferof products between two modes of transport,particularly between land and sea transport (forexample, Kozan, 1997; Tahar and Hussain, 2000).Although not specifically despatch bays, similarprocesses are found in transhipment betweendifferent modes.

3. Developing the methodology

The above review highlights some of the mainmethodologies that could be used in the context ofanalysing and improving despatch bay perfor-mance. However, it was felt that there were anumber of refinements that could be made to delivera more robust approach suitable to the needs ofindustry. The main concern related to the timerequired in developing a simulation model. As notedby De Vreede et al. (2003), the modelling processcan take longer than is felt acceptable by industry.However, it still provides a valuable tool in order totest improvements within a dynamic environment.Therefore, the methodology in this research aimedto use techniques that would enable suggestions tothe industrial partner during the research process,and yet, still provide the information required todevelop a DES model in the longer term. Aschematic of the methodology adopted can befound in Fig. 2.

Value StreamMapping

Process ActivityMapping

Feedba

Fig. 2. The research

The first stage involves developing a value streammap (VSM). The aim of this technique is to providea single-sheet overview of the processes involved inthe whole supply chain. Rother and Shook (1998)introduced a standard set of symbols that enable adetailed view of processes within organisations.Being an interface, it is important that the despatchbay is understood within the context of the supplychain under investigation. It may be that itsperformance is affected by upstream or downstreamprocesses. The VSM provides the means to achievethis. Further, the scope of the later research activitycan be identified.

The next stage involves carrying out processactivity mapping (PAM; Hines and Rich, 1997) toprovide a step-by-step analysis of the activitiesmaking up the despatch process. With this, infor-mation about the average time taken and resourcerequirements is noted for each step. These are thenclassified as operation, transport, inspection, sto-rage or delay. The aim is to minimise the delay timeand maximise the operation time. As well as lookingat the time spent on various activities, costs can alsobe calculated by multiplying the duration of theactivity by the number of people required to carry itout and the labour cost per hour. This effectivelyresults in an activity-based cost. From this mapping,it is possible to identify improvements to thedespatch process by looking to eliminate wasteactivities (such as delays). Future scenarios aredeveloped and appraised on the basis of the averagetimes and costs, providing an early output for theindustrial partner. The weakness of doing this isthat, in a dynamic environment, the benefitsachievable may be less. However, there should besome ‘quick wins’ available that will deliver benefitsto the industrial partner without developing a full-simulation model. To determine the feasibility ofimplementation, the industrial partner should re-view all the solutions.

Thirdly, statistical analysis of data related to thedespatch operation is carried out. The data collected

Discrete EventSimulation

DataAnalysis

ck

methodology.

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Table

1

Comparisonofmethodologyto

actionresearchandother

diagnostic

tools

Stages

ofsupply

chain

diagnostic

andim

provem

entmethodologies(FahmySalamaandTowill,2005)

Des

patc

hB

ay

Met

hodolo

gy

Project

proposal

Define

scopeof

analysis

Perform

ance

analysis

Identify

painsor

symptoms

Data

collection

Data

analysis

Solutionor

enabler

development

Evaluation

andrisk

analysis

Presentation

ofresults

Implementation

Post

implementation

Act

ion

rese

arc

hcy

cle

(CoughlanandCoghlan,2002)

Contextandpurpose

||

Scopingmeetings

Data

gathering

||

VSM,PAM

Data

feedback

|Feedback

Data

analysis

|Data

analysis,DES

Actionplanning

||

|Feedback

Implementation

|Evaluation

|D

espatc

hbay

met

hodolo

gy

Scoping

meetings

Scoping

meetings

Data

Analysis

VSM,

PAM

VSM,

PAM

Data

analysis

DES

DES

Feedback

A. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–91 85

should also be useable within the simulation modelitself, so as to minimise the requests placed upon theindustrial partner. The analysis provides a detailedinsight into the behaviour of the despatch processand the characteristics of the despatches. Theopportunity for triangulation against the processmapping also exists. The data analysis shouldidentify areas for improvement that may not beevident within the PAM. Again, the findings shouldbe presented back to the partner. Often managersdo not have the time to study time series data orconduct in-depth analyses. Therefore, this part ofthe process can provide a lot of benefit to managers.Anomalies, distortions or omissions in the analysiscan also be picked up through the feedback process,helping to increase the validity of the data used inthe simulation.

The carrying out of process mapping and dataanalysis is similar to the approach of the Quick Scan(Lewis et al., 1998), and has the same intention—toprovide improvement suggestions within a shortperiod of time. Also, Dawood and Marasini (2001)used data analysis as a precursor to their simulation,but did not conduct PAM.

The final stage of the methodology is to develop aDES model of the despatch process to test thepotential improvements and quantify the benefitsthey deliver within a dynamic environment. One ofthe weaknesses of PAM is that it focuses on theaverage values, and so does not capture either theresponse to dynamic behaviour or the interactionbetween call-offs as they pass through the despatchprocess. This is particularly important when thereare shared resources that may extend activity timesdue to queuing. The modelling is based around thePAM, although the individual steps are groupedinto activities. This makes model developmenteasier and reduces the data requirements. Themethodology in this paper is different from manyof the other DES models, which use interviews toconceptualise them (De Vreede et al., 2003).Although not included in this model, it could bepossible to include activity-based costing to evaluatethe impact of the proposed changes (Spedding andSun, 1999).

4. Comparison to action research

Recent years have seen an increased appreciationof the need for more qualitative research within thefield of logistics, and in particular action research(Naslund, 2002). One of the benefits of this

ARTICLE IN PRESSA. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–9186

approach is a greater interaction with industry, asthey are actively involved in the research as opposedto in case studies, where they would be passivelyobserved. The research presented in this paper wascarried out as part of a three-year research projectfunded by the UK Government, focussing onintegrating transport more fully into the supplychain. A key element of the project was the use ofaction research, so as provide benefits to industry,as well as developing academic theory.

Coughlan and Coghlan (2002) propose a cyclicalapproach to action research. This starts with thedetermination of the problem to be identified, andleads on to a data collection phase, where theresearcher becomes actively involved within theparticipating company. An important next stage isthe feedback of this information before analysis isdone. The data analysis should result in thedevelopment of an action plan for implementation.The final stage is to evaluate the success of theimplementation, which may then require furtherresearch to address additional problems that arise.These seven stages are also reflected in the mainsupply chain improvement methodologies reviewedby Fahmy Salama and Towill (2005). Table 1 showshow these overlap with each other.

Also mapped onto Table 1 is the methodologydeveloped in this paper. As can be seen, themethodology is reflective of both dimensions ofthe framework. Although not detailed as part of themethodology above, there were a number of meet-ings between the industrial partner and the researchteam in order to identify the aims and expectationsof the project. These also facilitated the datacollection stage by ensuring participants were awareof the research. The methodology does not includethe implementation and evaluation elements ofaction research, as these were beyond the scope ofthe project with the industrial partner. Therefore,a future improvement to the method could be toinclude this phase.

5. Application of the methodology

Having outlined the proposed methodology, thepaper now demonstrates its applicability withinthe context of a steel processor. The aim is todemonstrate how the different methods complementeach other, providing both additional insights, as aresult of each stage, as well as triangulation betweenthe different tools.

5.1. VSM

As outlined above, the VSM covered all theoperations of the case study company. This wasparticularly useful in understanding how the pro-duct moved to the despatch bay locations; there arefour of these bays on the site. The VSM for thecompany is shown in Fig. 3. The steel companyreceives orders from customers. This information isthen used to determine the production batch size foreach product. The production process involvestaking a coil of steel and slitting it into narrowstrips. These are then formed using dies into steeltubes, 85% of which then passes into stock awaitingdespatch. 15% is subjected to further processing(termed ‘finishing’), either at the request of thecustomer or due to a manufacturing defect. Oncemanufactured, the steel is put into stock. Thestockholders can then review all products in stock,and decide which they want delivered and when.This information is passed to the steel processor,and is known as a call off. Deliveries are then madeby road using third-party hauliers.

For raw material replenishment, the steel proces-sor places orders on the supplier of steel coils (calledStripCo for anonymity). The suppliers of steel coilshave an arrangement whereby they can transportcoils to the steel company as they are produced andhold them in stock there until required. However,they remain within the ownership of StripCo.

5.2. PAM

The scope of the PAM was defined as thedespatch process, including the interactions withboth the stockholder and the haulier. Originally, thestarting point for the mapping was the sending ofstock prints to the stockholder. However, this thenrequired the inclusion of the time spent waiting for acall off in response to this fax. Consequently, therewas some distortion of the findings, especially as thetransport clerk was doing other activities not relatedto the despatch process while waiting. Therefore,the scope of the mapping was redefined as from thepoint where a call off is received. This is thenconverted into a loading instruction that specifiesthe bundles available to load and their locationwithin the warehouse. This is then collected by thewarehouseman, who carries out a stock check toensure that there are no quality issues with theproducts and that they are in the locations stated onthe loading instruction. Once this is in order, an

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Delivery

StripCoCommercial

ProductionPlanning Data

Processing

Sales Stockholder

Slitter

Coilstore

Slitcoil

store

StripCostore Tube

MillCrane45km

StripCoTransport

Despatch

Haulier’sTransport

Office

OrderOrderCoil orderWeekly Order

Rolling Plan

Daily stock printCall off

Loading Note

LoadingNote

Productionplan

Productionplan

Crane Crane

85%

Productionplan

Crane

15%

Rolling Plan

Warehouse

Processing Inventory PhysicalFlow

RoadTransport

InformationSystem

Information Flow

Electronic Manual

Finishing

CraneFor

klift

Truck

Fig. 3. Value stream map of the case study steel company.

Table 2

The proposed improvements from the process activity mapping

Short-term improvements Medium/Long-term improvements

Automatic stock prints Handheld scanners

Accurate bundle information Automatic despatch notes

Loading from one bay only Relocated weighbridge button

A. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–91 87

empty trailer is requested from the haulier. This isweighed in and placed in the relevant loading bay.Loading then takes place and upon completion thetrailer is removed and weighed out. The finalactivity is the generation of a despatch note, whichis then passed to the haulier.

The mapping was entered into a spreadsheet, andthe time for the whole process and each category ofactivity calculated. From the mapping, a number ofshort- and medium-/long-term improvements werebrainstormed; the categorisation being related towhether investment was required, and the techno-logical capabilities of the steel processor and theircustomers. These were refined to the improvementsdetailed in Table 2, incorporated into the spread-

sheet, and the process and activity category timesrecalculated. The results of this analysis can be seenin Fig. 4. It should be noted that the total time is forthe whole despatch process and not just the time avehicle takes to get loaded. Also, there was nostorage within the despatch process, and so is notincluded in the diagram.

As can be seen, the process mapping highlightsthe opportunity to remove a significant amount oftime from the despatch process. Much of thisreduction comes in the information flow, particu-larly with regard to the ability to remove processesas a result of improved information availability.There is a substantial gain predicted from imple-menting the short-term solutions, while the gainfrom the short term to the medium term is not solarge. Looking at the breakdown for the differenttypes of activity, it can be seen that there is littlechange between the three scenarios. This is partiallybecause some of the activities that currently addvalue due to the technological capabilities of thecase study company become non-value adding inthe new scenarios due to changes in the process.

One disadvantage to the above analysis is that itutilises average values for the time products pass

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Medium/Long Term

Current

Operation

Transport

Inspection

Delay

Total Time

333 minutes

Total Time198 minutes

Total Time245 minutes

Short Term

Fig. 4. Process activity mapping results for current and future scenarios.

A. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–9188

through the processes. In carrying out the mapping,it became evident that there was significant varia-tion in the times, which required further analysisusing data. The impact of interactions betweendifferent call offs flowing through the system andsharing resources is not captured, which encouragedthe development of a simulation model. This wasparticularly important in the context of this researchas the steel processor was looking to eliminateovertime working. Just multiplying the average timeby the number of despatches in a week would notgive an accurate insight into whether this wasachievable.

5.3. Data analysis

In terms of data analysis, information aboutdespatches over the course of a month was gathered.In total, this covered 308 despatches. The data wasanalysed in a number of different ways. Firstly,descriptive statistics were used, looking at the meanand distribution of the time taken to load, thenumber of bundles and products per load, and thenumber of loading bays visited. Some of theseresults are shown in Fig. 5. The distributions wereplotted, as it was felt that descriptive statistics alonemay be distorted and present the wrong picture. Forinstance, the average time taken to load is 3 h24min, yet there is a long tail to the distribution,

which will increase the mean. Most of thedespatches are, in fact, completed in less than 4 h.

In addition, correlation analysis using Spear-man’s rank was carried out between these variables.This analysis found statistically significant relation-ships between the time taken to load, the number ofbays visited and the number of different products ona load. These variables were inter-dependent. Thisinter-dependency meant that a multiple regressionanalysis carried out yielded no useful information.By carrying out the data analysis, it was possible toget a clear picture of the behaviour of the despatchprocess. This level of insight was found to beextremely useful for the management of the casestudy company and provided both additional‘quick wins’ and further ideas for testing withinthe simulation model.

5.4. Simulation

The final stage of the methodology is simulation.This section will only provide an overview of themodelling process and findings; for more details,please refer to Yang et al. (2004). As already noted,the model itself was based around the PAM.However, to simulate each individual step wouldbe both time consuming and require extensive datacollection at the steel processors. Therefore, thesteps were grouped together so that distinct

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Mean Distribution

Tim

e ta

ken

to lo

adde

spat

ch3h24m 0

1020304050

<1h

r

<2h

r

<3h

r

<4h

r

<5h

r

<6h

r

<7h

r

<8h

r

<9h

r

<10

hr

Duration

Num

ber

of

desp

atch

es

Bun

dles

per

des

patc

h

16.4

01020304050

7 11 13 15 17 19 21 23 25

Number of Bundles

Num

ber

of

desp

atch

es

Pro

duct

s pe

r de

spat

ch

3.24

0

20

40

60

80

Number of Products

Num

ber

of

desp

atch

es

1 2 3 4 5 6 7 8 9 10+

9

Fig. 5. Descriptive statistics from the case study company.

Table 3

Results from the simulation modelling

Scenario Time to process call off (min) Time to load 1 weeks worth of

despatches (min)Process activity mapping Simulation

Current 333 359 4214

Short term 245 275 4050

Medium/Long term 198 229 4049

A. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–91 89

activities could be identified. In addition, referencewas made to the data analysis to ensure that anytiming points were included. This enabled the use ofdistributions derived from real data. In some cases,additional data was obtained while some assumeddistributions also had to be included. The modelwas developed in the simulation software packageArena, and subsequently verified and validated.

Experimentation was based around testing eachsuggested improvement individually as well as incombination with one another. In addition, adeadline for call offs was introduced. The impactof this would not be captured within the processmapping, as this commenced with the receipt ofthe call off. However, it could be reflected in thesimulation model, through altering the arrival rate

ARTICLE IN PRESSA. Potter, C. Lalwani / Int. J. Production Economics 106 (2007) 82–9190

of call offs. The results are presented in Table 3,including a comparison with the PAM results. Thesimulation results actually allow for the stock printsnot being 100% accurate. This was felt to moreaccurately reflect an achievable level within the casestudy company, and does deliver most of thebenefits. If 100% accuracy was assumed, then thetime to process a call off in the medium/long-termscenario becomes 216min, with the time to load aweeks worth of despatches at 4051min. Whencomparing the PAM and simulation values, it canbe seen from Table 3 that there is a close similaritybetween them. What the simulation demonstratedwas the importance of having a deadline for calloffs. Without the deadline, the model showed thatovertime would be required to load all despatches,but its introduction makes it possible to avoid thisextra expense.

6. Conclusion

Despatch bays are an important part of anyorganisation’s operations, yet are often a source ofdelays within the flow of products through thesupply chain as vehicles and products wait to beloaded and despatched. This can then negativelyimpact upon customer service levels. This paperpresents a robust methodology that aims to facil-itate the improvement of physical and informationflows in this area. Unlike much previous work, thismethodology combines VSM, process mapping,data analysis and DES to deliver solutions not onlyat the end of the research, but also while the work iscarried out. This makes the approach more attrac-tive to industrial partners who often look for quickresults, and is also reflective of action research.Mapping the methodology onto the action researchcycle outlined in Coughlan and Coghlan (2002)shows this overlap. The main gap in the methodrelates to the implementation and evaluation phasein action research. This was beyond the scope of theresearch presented in this paper, but represents anopportunity for further improvement of the meth-odology.

The application of the methodology to a casestudy steel company is described, demonstrating thevalue of the approach. In particular, the companyfound the regular provision of feedback to be useful,enabling action to be taken before the completion ofthe whole research process. The initial mappingwork suggested that a 26% reduction in the time todespatch a vehicle could be achieved by removing

waste from the process, with a further reduction of20% achievable with investment in technologicalimprovements. The data analysis provided insightsinto the behaviour of the system. This data was thenused in the simulation model. The results from thisshowed that the time reductions predicted throughthe mapping were potentially greater than could beachieved, due to queuing within the system as wastewas removed.

Because of the general nature of the techniques, itis possible to apply the method more widely to otherdespatch bay locations, including the ones inwarehouses. In doing so, the researcher needs toensure that the scope of the study is clearly defined,to ensure that the models do not become too largeand data collection onerous. The availability oftiming data is also important for both developingand validating the model.

Future research will look to apply the methodol-ogy to other despatch bay scenarios, in order togeneralise the approach. In addition, this will enablethe identification of common elements and pro-cesses, which could lead to a generic ‘despatch bay’model. Finally, an appraisal of the success (orotherwise) of the changes at the steel processorresulting from the implementation of this piece ofresearch will be made. This will complete the loopcommon in action research, providing a morecomplete methodology.

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