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International Journal of Lean Six SigmaEmerald Article: A Six Sigma framework for marine container terminals

Amir Saeed Nooramin, Vahid Reza Ahouei, Jafar Sayareh

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A Six Sigma framework formarine container terminals

Amir Saeed NooraminFaculty of Maritime Economics and Management,

Khoramshahr University of Marine Science and Technology,Khoramshahr, Iran, and

Vahid Reza Ahouei and Jafar SayarehFaculty of Marine Engineering, Chabahar Maritime University, Chabahar, Iran

Abstract

Purpose This research uses an optimisation model, based on the Six Sigma methodology, whichassists marine container terminal operators to minimize trucks congestions, as a defect in the global

containerisation and smoothing the gate activity to reduce trucks turn-around times. The mainpurpose of this paper is implementing the Six Sigma in the landside of marine container terminals toreduce the average number of trucks in queues and average trucks waiting times in both entrance andexit gates.

Design/methodology/approach This study examines the applicability of the DMAIC methodalong with the SIPOC, cause and effect diagram, and failure mode and effect analysis (FMEA).

Findings In this paper, Six Sigma methodology is found as an accurate optimisation tool in marinecontainer terminals. Risk Priority Numbers obtained from the FMEA analysis denote that additionalcontrol procedures and associated inspections are needed as monitoring tools on the working time andactivity of weighbridge operators and trucks drivers. In addition, serious consideration should begiven to operators performance appraisal and improving the administrative systems.

Research limitations/implications This study was carried out with some boundaries; like thecomplex operational system in marine container terminals, available data, time constraints, training

the team members and controlling the implemented obtained results.Originality/value To date, no study has adequately examined the Six Sigma methodology inmarine container terminals as an optimisation tool for reducing trucks congestion. The challengingissues inherent this problem and the limitation of existing research, motivates this study.

KeywordsSix Sigma, DMAIC, FMEA, Container terminal, Truck congestion, Turn time, Iran

Paper typeResearch paper

1. IntroductionIn general, container terminals can be described as open systems of material flow withtwo external interfaces. These interfaces are the quayside designed for loading andunloading of ships and the landside where containers are loaded and unloaded on/off

the trucks (Steenken and Vob, 2004). Most terminals are taking measures to increasetheir throughput and capacity by (Huynh and Walton, 2005):

. introducing new technologies;

. optimising equipment dwell-times;

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/2040-4166.htm

The authors would like to express their gratitude to Professor Antony, University of Strathclyde,Professor Gitlow, University of Miami, and Dr Banuelas, Rolls Royce plc., for their invaluablecomments, which improved the quality of the paper.

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Sigma

Vol. 2 No. 3, 2011

pp. 241-253

q Emerald Group Publishing Limited

2040-4166

DOI 10.1108/20401461111157196


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. increasing storage density;

. optimising ship turn-around times; and

. optimising truck turn-around times.

In todays global marketplace, container terminals are regarded as Server-Customer(Queue) systems wherein servers and customers are variable based on the differentoperational viewpoints. Figure 1 shows the Server-Queue system designed based onthe purpose of this study.

In this paper, Six Sigma methodology is used to find and reduce defects in the server(gate area) and improve customer satisfaction via decreasing the turn-around time ofthe trucks, trucks queue and reduction of the overall transfer cost of containers in theirsupply chain cycle.

2. Review of related literatureA great variety of container terminals exists, mainly depending on which type of

handling equipments combined to form a terminal system. Khoshnevis and Asef-Vaziri(2000) defined three performance analysis variables including throughput, spaceutilisation and equipment utilisation. Kozan (2000) discussed the major factorsinfluencing the transfer efficiency of seaport container terminals by developing anetwork model. Nishimuraet al.(2001) implemented Lagranges method for optimisingthe container yard operation. Similar studies in this field have been carried out by Namand Ha (2001), Lieet al.(2002), Vis and De Koster (2003) and Murtyet al.(2003).

Berth planning problems may be formulated as a different combination of optimisationproblems, depending on the specific objectives, and restrictions that have to be observed.Legato and Mazza (2001), Nishimuraet al.(2001), Imai et al.(2005) and Moorthy and Teo(2006) have all carried out numerous studies on berth planning problems. Lee and Chen(2009) have optimised the berth operation by evaluating different arrival patterns.

Nowadays, the logistics activities, especially at large container terminals, havereached a degree of complexity that further improvements are required for the interactionof scientific solutions. Simulation models have become the viable tools for decisionmaking in port activities. Kia et al. (2002) have investigated the role of computersimulation in evaluating the performance of a containerterminalin relation to its handlingtechniques and the impact it makes on the capacity of terminal. Parola and Sciomachen(2005) have presented a discrete event simulation modelling approach related

Figure 1.Server-Queue systemin marine containerterminals

Server

(gate)

Queue

(waiting area)

Lane change and

truck turning area

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to the logistic chains of an intermodal network. Bielli etal. (2006) have provided a help-toolin a port decision support system implementing simulation via Java environment.Froylandet al. (2008) have presented an algorithm to manage the container exchangefacility, including the allocation of delivery locations for trucks and other container

carriers. Zeng and Yang (2009) have developed a simulation optimisation method forscheduling the loading operations in container terminals.

The time trucks spend at a terminal for loading/unloading of cargo(truck turn-around time) is a real cost scenario which affects the overall cost of thecontainer trade. Historically, truck turn-around times have received a very little attentionfrom terminal operators because landside congestions have never been a barrier to theirsmooth operations. Truck turn-around times are the times that a truck takes to completean activity such as picking up an import container. As shown in the studies conducted byRegan and Golob (2000), Klodzinski and Al-Deek (2002) and Huynh and Walton (2005), byoptimising the truck turn-around times and thereby the landside shipping cost, theterminals would gain a competitive advantage in the industry. Murty et al. (2005) havedescribed a variety of inter-related decisions made during daily operations at a container

terminal. Their goal was to minimise the waiting time of customer trucks.To date, no study has adequately examined the philosophy of Six Sigma in marine

container terminals as a managerial decision-making optimisation tool instrategic/operational levels. The challenging issues inherent this problem, and thelimitation of existing research, robustly motivates this study.

3. Problem statement for the case studyThe objective of this case study is to minimise trucks congestions at the main gates ofthe container terminal of the Shahid Rajaee Port Complex (SRPC), the major Iranianseaport, and hence to reduce the trucks turn-around times.

Generally, weighbridges are regarded as one of the main hindered movement

stations in port operation, which cause long queues of trucks. The SRPC is equippedwith six main automatic weighbridges in following patterns:

. two are located near the main entrance of the gate complex;

. two are located near the exit gate; and

. two are located at the transit yard where only one of them is operational.

Even though the case study is unique and distinctive of its kind, the general processesand characteristics are similar to a typical container terminal as shown in Figure 2.

Since there are usually long queues of trucks waiting in the container yard forweighting operation, this case study develops a Six Sigma model to find problems,defects and barriers in weighting operation, and proposes operational solutionsfor reducing trucks waiting times via smoothing the gate activities.

Figure 2.Process of

loading/dischargingoperation in marinecontainer terminals

Enter

ContainerExit

Road trucks

Road trucks

Delivering export

containers

Picking up import

containers Quaysideoperation

Road/vessel crane

Wieghbidges

Gate

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4. Case studyThis case study examines bottlenecks in the loading and/or unloading process byexamining the following four main patterns with the objectives of reducing trucksturn-around times:

(1) arrival pattern of trucks at the main entrance of the gate complex;

(2) service pattern of weighbridges located at both the entrance and the transitgates;

(3) departure patterns of trucks at main gate exit; and

(4) service patterns of weighbridges located at the main exit of the gate complex.

The data gathered from the container terminal of the SRPC during January2008-December 2009 and are used for evaluation of test cases.

This study examines the applicability of the DMAIC method using the followingtools:

. supplier input process output customer (SIPOC) chart;

. cause and effect diagram; and

. failure mode and effect analysis (FMEA).

Indeed, the objective of this research is to reduce the truck congestion in the transit,exit and entrance gates of SRPC, using the DMAIC method.

4.1 Define phaseMarine container terminals can be adequately modeled as supplier-customer systems.Within them, different service patterns exist; thus SIPOC charts can be used foranalysing their vast operations. Figure 3 shows the SIPOC chart of the case study.

Analysis of the SIPOC chart proves that the optimisation of weighing operation is

an important step for reducing congestion, achieving customer satisfaction and savingtimes/costs at loading/unloading operation of trucks. So, Critical to Quality (CTQ) willbe the waiting time of trucks, which are weighed at both entrance and exit gates.

Figure 3.SIPOC chart(loading/unloadingoperation of trucks)

Supplier Input Process Output Customer

Port operators

Cargo owners

Transport

companies

Shipping lines

Operators

Container

Bill of Lading

(B/L)

Truck

Container

Truck

Train

B/L

Cargo owners

Railway

Transport

companies

Freight

forwarders

Inspection

(by security

guards)

Weighting

reciept

Weighting

operation

Administrative

processing

Loading

unloading

Administrative

processing

Inspection

(by security

guards)

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With respect to the defined CTQ, data collection phase was planned aiming to gatherdata on waiting time of the entrance and exit gates for weighing operation of trucks.

4.2 Measurement phase

According to the definition of CTQs at the previous section, data for waiting time oftrucks in weighing operation at entrance and exit gates have been collected and shownin Figures 4 and 5, respectively, using the MINITAB software.

The mean and standard deviations (SD) at the entrance gate are equal to 274.5 and218.9, respectively. Figure 6 shows the individuals and moving range (I-MR) chart forthe waiting time of entrance gate baseline.

Figure 6 shows that the process mean and variation of waiting time of entrance gate isnot stable. The points of 50, 86.108, 162 and 219 in MR chart and a few ranges in I chartare out of control which do not reveal any obvious cause of variation and process mean.

Values of the mean and SD for waiting time of exit gate are 777.3 and 531.9,respectively. Figure 7 shows the I-MR chart of waiting time of exit gate.

Figure 4.Waiting time histogram oftrucks at the entrance gate

50

Histogram of waiting time of entrance gate

Waiting time of entrance gate (second)

40

30

20

10

Frequency

00 100 200 300 400 500 600 700

Figure 5.Waiting time histogram of

trucks at the exit gate

18

16

14

12

10

8

6

4

2

0 400 800 1200 1600

Waiting time of exit gate (second)

Histogram of waiting time of exit gate

Frequency

0

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The above I-MR chart indicates that the process mean and variation of waiting time ofentrance gate is not stable. The point of 40 in MR chart and the ranges between 1 and41, also 49 and 78 in I chart are out of control which do not reveal any obvious cause ofvariation and process mean.

Table I represents the DPMO before and after process improvement for main CTQs.Project objective is to reduce the percentage of trucks waiting time in the entrance

Figure 6.

I-MR chart for baselinewaiting time of entrancegate data

800

600

400

200Individua

lvalue

0

800

600

400

200Movingrange

0

1 25 49 73 97 121

Observation

I-MR chart of waiting time of entrance gate

145 169 193 217

1 25 49 73 97 121

Observation

145 169 193 217

UCL = 366.5

UCL = 113.0

X = 274.5

MR = 34.6

LCL = 182.5

LCL = 0

Figure 7.Individual and movingrange chart for baselinewaiting time of exit gate

data

200

150

100

50

0

1 9 17 25 33 41 49 57 65 73

Observation

UCL = 142.2

LCL = 0

Movingrange

MR = 43.5

22222

1

1,600

1,200

800

400

0

1 9 17 25 33 41 49 57 65 73

Observation

I-MR chart of waiting time of exit gate

UCL = 893

LCL = 662X = 777

Individualvalue

111111

1111 1111111111

111

111111 11

1111

51

1111

111 11111

111

11111

11111111 1

22222

IJLSS2,3

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and exit gates, which are more than 330 and 990 seconds, respectively, to 0.62 per cent

for access to four sigma.

Cause and effect diagram is an analysis tool that provides a systematic way of

looking at the effects and at the causes that create or contribute to those effects (Kumar,

2006). Figure 8 shows the cause and effect diagram of the SRPC which is designedbased on the SIPOC chart.

As shown in the Figure 8, there are four main factors which cause the truck

congestion in the SRPC. These include:

(1) port operators which work on different parts of the SRPC;

(2) port equipments (including both the hardware and software);

(3) trucks and their drivers; and

(4) owners of import/export/transit containers.

4.3 FMEA

FMEA is a structured and qualitative analysis of a system or function which identifiespotential system failure modes, their causes and the effects on the system operation

associated with the failure modes accuracy (Gitlow and Levine, 2004; Kumar, 2006).

Table II tabulates the FMEA of the SRPC problem, obtained according to the results of

group brainstorming among the experts of the container terminal of the SRPC, based

on the cause and effect diagram.

Yield DPMOCTQs Current (%) Desired (%) Current Desired

Waiting time of entrance gate 60 99.38 400,000 6,210Waiting time of exit gate 65.6 99.38 344,000 6,210

Table I.Current and process

performance for CTQs

Figure 8.Cause and effect diagram

(loading/unloadingoperation of trucks)

Truck congestion

Port operators

Trucks

Port equipment

Cargo owners

Working hours

Accuracy

Proficiency

Security guards

Crane operators

Weighbridge operators

Hardware and software

Weighbridges

Landside cranes

Exhaustion

Service patterns

EDI implimentation

Equipment defects

Port formalities

Custom formalities

Bill of Lading

Exit pattern of trucks

Arrival pattern of trucks

Traffic signs

Drivers

Enter/exit processes

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The analyse phase involves identifying the upstream variables (Xs) for each CTQ.Upstream variables are the factors (Xs) that affect the performance of a CTQ (Gitlow,2009). According to the results of the FMEA, followings are the main roots (Xs) ofcongestion in the landside:

. X1 Working time of weighbridge operators (Risk Priority Number(RPN) 640): total working time of weighting operation during a workingday, X1 0 when weighing operation time matches the working time of port.

. X2 Activity of weighbridge operators (RPN 490): efficient work ofoperators during a working day, X2 0 when weighbridge operators havedone their job efficiently.

. X3 Administrative processing (RPN 448): customs formalities for cargo

clearance and terminal formalities for transport documents such as bill of ladings(B/Ls), X3 0 when both, the customs and port formalities, are doneelectronically based on the electronic commerce principles.

. X4 Trucks driver (RPN 405): familiarity of drivers with port environment,X4 0 when trucks divers are familiar with port area and its formalities.

. X5 Operators accuracy (RPN 336): accuracy of weighbridge operators indoing their job with no error, X5 0 when weighbridge operators are accurateand there is no claim on their work.

Failuremode Potential effect Severity Potential cause Occurrence

Currentcontrol Detection RPN

Truck

congestion

Dissatisfaction

of customers

10 Working time of

weighbridgeoperators

8 Indirect

supervision

8 640

Activity ofweighbridgeoperators

7 Indirectsupervision

7 490

Accident 9 Traffic signs 6 Videosupervision

3 162

Trucks drivers 5 No enoughsupervision

9 405

Human errorand fatigue

8 Operatorsaccuracy


7 336

Activity ofweighbridge

operators


6 240

Financialpenalties

7 Administrativeprocessing


8 448

Increment oftrucks waitingtime

6 Weighbridgesmalfunction

5 PM 5 150

Operatorsaccuracy


7 84

Containerdwell time

5 Administrativeprocessing


6 240

Driversconfusion

3 Traffic signs 4 Videosupervision

3 36

Table II.FMEA for truckcongestion at weighingoperation step

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Figure 9 shows the Pareto chart for the main Xs.RPNs obtained from the FMEA table and Figure 9 denote that additional control

procedures and associated inspections are needed as monitoring tools on the workingtime (X1) and activity of weighbridge operators (X2). Furthermore, administrative

systems (X3) and customs formalities should be under an accurate control system.In addition, serious consideration should be given to trucks drivers (X4) and operatorsaccuracy (X5).

4.4 Improvement phaseImprovement phase focuses on reducing the amount of variations found in the CTQ bymanipulating the five critical Xs; that is, X1 through X5. The main concept behind thisphase in the DMAIC method is that the suggestions are based on the analysis of thecause and effect diagram and the FMEA table.

The results of the FMEA suggest that the most relevant potential causes to addressare operators working time (X1) and their activity (X2). The obtained results imply

that there should be changes made to the weighbridge operators process and weightingprocess aiming to decrease variation in the CTQ.

4.5 Control phaseThe purpose of the control phase is to make sure that improvements are sustained andreinforced (Antony et al., 2006). In this phase, based on the FMEA, the followingnecessary improvement and control actions are defined, as shown in Table III.

Control and improvement plans are defined based on the results of a workshopamong experts of SRPC. As shown in the Table III, suggested plans include operational

Figure 9.Pareto chart of main roots

(Xs) of congestion

3,000

Main roots (Xs) of congestion

RPNs 640 490 448 405 336 162 150

5.76.212.815.417.018.624.3Percent

24.3 42.9 60.0 75.4 88.1 94.3 100.0Cum %

Worki

ngtimeo

fweig

hbrid

geopera

tors

Activityo

fweig

hbrid

geopera

tors

Adm

inistr

ative

process

ing

Trucks'

drive

rs

Opera

tors'a

ccurac

y

Traffic

sign

s

Weig

hbrid

gesm

alfun

ction

100

80

60

40

20

0

(%)

RPNs

2,500

2,000

1,500

1,000

500

0

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solutions on direction sign installation, administrative solutions on training andsuggestions to improve cooperation among different authorities such as customs andterminal.

Among all suggested plans for Xs, the X3 has long-term solution, i.e. itsimprovement plan needs a long period of time for accomplishment, because it needs anadministrative cooperation between customs and terminal authorities. Thus, due tolack of sufficient time, we have to only control X1, X2, X4 and X5.

4.6 Achieved benefitsNumber of queuing lines and average waiting time of trucks are regarded as the mainparameters of a queuing system. Table IV presents the results of implementing theproposed plans and their effects on reducing truck congestion in the port area.

As stated in Section 4.5, due to time constraint, only improvement plans for X1, X2,X4 and X5 are accomplished in SRPC. The control plan data in Table IV are gatheredone month after implementing the proposed solutions for Xs, by Six Sigma teammembers. As shown in Table IV, accomplishing improvement plans, in the case studyand controlling them regularly, caused a sensible reductions in truck congestion inboth of the entrance and exit gates weighbridges.

5. ConclusionSix Sigma is an accurate systematic framework for quality improvement and businessexcellence, which has never been academically used in marine container terminals.This paper proposed a Six Sigma methodology aiming to reduce truck congestion inmarine container terminals via smoothing the gate activities, in particular weightingprocess of trucks carrying import/export/transit containers.

The DMAIC method along with the SIPOC chart, cause and effect diagram, andFMEA are used as analyses tools in this research, focusing on managerial operations inthe entrance and exit gates of the SRPC as the case study.

Item What is controlled? Requirements Improvement plan Control methods Frequency

X1 Working time ofweighbridge

operators

Three workingshifts for

operators

Adjusting weighingoperation with

working time of port(24 hour)

Indirectsupervision by

employer

Weekly

X2 Activity ofweighbridgeoperators

Good trainedoperators

On the job training Directsupervision byemployer

Daily

X3 Administrativeprocessing

Cleardocumentation/Electronicdocumentation

Using electronic B/Lsand more coordinationbetween customs andport

Directsupervision bymiddle and topmanagers of port

Monthly

X4 Trucks driver Enoughdirection signsin port area

Install more directionsigns in port area

Directsupervision byport patrols

Daily

X5 Operators accuracy Good trained

operators/reports ofcustomers

On the job training Direct

supervision byemployers

Per

workingshift (threetimes/day)

Table III.

Control and improvementplans for the criticalfactors

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Working time, activity and accuracy of weighbridge operators, drivers of trucks andadministrative processing were the main causes of trucks congestion in the SRPC.

According to the obtained results, followings should be considered for reducingtrucks congestion:

. There should be more control on the weighbridges working time.

. The service pattern of weighting operation should be modified and changed tothe normal distribution.

. The activity of weighbridge operators should be under an accurate control system.

. There should be new traffic signs in the landside area, aiming to reduce driversconfusion with the processes.

. EDI should be implemented in the administrative processing, especially customsformalities and B/Ls.

Accomplishing the improvement plans in the case study have caused sensiblereductions in transit, entrance gate and exit gate weighbridges.

Six Sigma is a statistic based analysis tool, which was imposed followinglimitations on this study:

. With respect to the complex operational pattern of marine container terminals,a vast range of data is necessary for an accurate Six Sigma analysis.

. Six Sigma requires massive training among team members, in particular in

implementation and control phases, which was imposed some delays duringresearch.

. Control phase is the main limitation of this research, wherein it demands a longperiod for implementing the obtained results of the study.

With regards to the mentioned limitations, it might be a good idea to model the controlphase with simulation software packages, such as Arena and Flexsim, and analyse thesimulated results with Six Sigma.

Item Present model Proposed plan Congestions reduction (%)

Weighbridgesof entrance

gate

Queuingnumber

Max.(Que.)

25 6 76

Ave.(Que.)

9.31 0.8 91

Waitingtime

Max.(Sec.)

727.6 103.2 86

Ave.(Sec.)

274.5 20.21 93

Weighbridgesof exit gate

Queuingnumber

Max.(Que.)

34 3.5 89

Ave.(Que.)

15.56 0.6 96

Waitingtime

Max.(Sec.)

1667.9 123.2 93

Ave.

(Sec.)

777.3 29.01 97

Table IV.Achieved benefits of

control and improvement

plans

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container terminal, Decision Support Systems, Vol. 39, pp. 309-32.

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Further reading

Goh, T.N. (2002), A strategic assessment for Six Sigma, Quality and Reliability Engineering

International, Vol. 18, pp. 403-10.Nishimura, E., Imai, A., Janssens, G. and Papadimitriou, S. (2009), Container storage and

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Corresponding authorAmir Saeed Nooramin can be contacted at: [email protected]

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