International Journal of Lean Six Sigma A Six Sigma framework for marine container terminals Amir Saeed Nooramin Vahid Reza Ahouei Jafar Sayareh

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A Six Sigma framework for marine container terminals

Six Sigma framework

Amir Saeed Nooramin Faculty of Maritime Economics and Management, Khoramshahr University of Marine Science and Technology, Khoramshahr, Iran, and

241

Vahid Reza Ahouei and Jafar Sayareh Downloaded by MAHIDOL UNIVERSITY At 12:13 31 January 2016 (PT)

Faculty of Marine Engineering, Chabahar Maritime University, Chabahar, Iran Abstract Purpose – This research uses an optimisation model, based on the Six Sigma methodology, which assists 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 main purpose of this paper is implementing the Six Sigma in the landside of marine container terminals to reduce the average number of trucks in queues and average trucks’ waiting times in both entrance and exit gates. Design/methodology/approach – This study examines the applicability of the DMAIC method along 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 marine container terminals. Risk Priority Numbers obtained from the FMEA analysis denote that additional control procedures and associated inspections are needed as monitoring tools on the working time and activity of weighbridge operators and truck’s drivers. In addition, serious consideration should be given to operator’s performance appraisal and improving the administrative systems. Research limitations/implications – This study was carried out with some boundaries; like the complex 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 in marine container terminals as an optimisation tool for reducing trucks’ congestion. The challenging issues inherent this problem and the limitation of existing research, motivates this study. Keywords Six Sigma, DMAIC, FMEA, Container terminal, Truck congestion, Turn time, Iran Paper type Research paper

1. Introduction In general, container terminals can be described as open systems of material flow with two external interfaces. These interfaces are the quayside designed for loading and unloading 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 increase their throughput and capacity by (Huynh and Walton, 2005): . introducing new technologies; . optimising equipment dwell-times; 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 invaluable comments, which improved the quality of the paper.

International Journal of Lean Six 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 today’s global marketplace, container terminals are regarded as Server-Customer (Queue) systems wherein servers and customers are variable based on the different operational viewpoints. Figure 1 shows the Server-Queue system designed based on the 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 of the trucks, truck’s queue and reduction of the overall transfer cost of containers in their supply chain cycle. 2. Review of related literature A 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, space utilisation and equipment utilisation. Kozan (2000) discussed the major factors influencing the transfer efficiency of seaport container terminals by developing a network model. Nishimura et al. (2001) implemented Lagrange’s method for optimising the container yard operation. Similar studies in this field have been carried out by Nam and Ha (2001), Lie et al. (2002), Vis and De Koster (2003) and Murty et al. (2003). Berth planning problems may be formulated as a different combination of optimisation problems, depending on the specific objectives, and restrictions that have to be observed. Legato and Mazza (2001), Nishimura et 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, have reached a degree of complexity that further improvements are required for the interaction of scientific solutions. Simulation models have become the viable tools for decision making in port activities. Kia et al. (2002) have investigated the role of computer simulation in evaluating the performance of a container terminal in relation to its handling techniques and the impact it makes on the capacity of terminal. Parola and Sciomachen (2005) have presented a discrete event simulation modelling approach related Queue (waiting area) Server (gate)

Figure 1. Server-Queue system in marine container terminals

Lane change and truck turning area

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3. Problem statement for the case study The objective of this case study is to minimise trucks’ congestions at the main gates of the container terminal of the Shahid Rajaee Port Complex (SRPC), the major Iranian seaport, and hence to reduce the truck’s 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 equipped with 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 processes and 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 for weighting operation, this case study develops a Six Sigma model to find problems, defects and barriers in weighting operation, and proposes operational solutions for reducing truck’s waiting times via smoothing the gate activities. Gate

Road/vessel crane Wieghbidges

Enter Exit

Road trucks

Road trucks

Delivering export containers

Picking up import containers

Container

Quayside operation

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to the logistic chains of an intermodal network. Bielli et al. (2006) have provided a help-tool in a port decision support system implementing simulation via Java environment. Froyland et al. (2008) have presented an algorithm to manage the container exchange facility, including the allocation of delivery locations for trucks and other container carriers. Zeng and Yang (2009) have developed a simulation optimisation method for scheduling 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 the container trade. Historically, truck turn-around times have received a very little attention from terminal operators because landside congestions have never been a barrier to their smooth operations. Truck turn-around times are the times that a truck takes to complete an activity such as picking up an import container. As shown in the studies conducted by Regan and Golob (2000), Klodzinski and Al-Deek (2002) and Huynh and Walton (2005), by optimising the truck turn-around times and thereby the landside shipping cost, the terminals would gain a competitive advantage in the industry. Murty et al. (2005) have described 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 in strategic/operational levels. The challenging issues inherent this problem, and the limitation of existing research, robustly motivates this study.

Figure 2. Process of loading/discharging operation in marine container terminals

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4. Case study This case study examines bottlenecks in the loading and/or unloading process by examining the following four main patterns with the objectives of reducing truck’s turn-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 transit gates; (3) departure patterns of trucks at main gate exit; and (4) service patterns of weighbridges located at the main exit of the gate complex.

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The data gathered from the container terminal of the SRPC during January 2008-December 2009 and are used for evaluation of test cases. This study examines the applicability of the DMAIC method using the following tools: . 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 phase Marine container terminals can be adequately modeled as supplier-customer systems. Within them, different service patterns exist; thus SIPOC charts can be used for analysing 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 saving times/costs at loading/unloading operation of trucks. So, Critical to Quality (CTQ) will be the waiting time of trucks, which are weighed at both entrance and exit gates. Supplier

Input

Output

Process

Cargo owners

Port operators

Operators

Container

Cargo owners

Container

Truck

Railway

Train

Transport companies

Bill of Lading (B/L)

Transport companies

B/L

Truck Shipping lines

Figure 3. SIPOC chart (loading/unloading operation of trucks)

Customer

Inspection (by security guards)

Weighting reciept

Weighting operation

Administrative processing

Loading unloading

Administrative processing

Freight forwarders

Inspection (by security guards)

4.2 Measurement phase According to the definition of CTQs at the previous section, data for waiting time of trucks in weighing operation at entrance and exit gates have been collected and shown in Figures 4 and 5, respectively, using the MINITAB software. The mean and standard deviations (SD) at the entrance gate are equal to 274.5 and 218.9, respectively. Figure 6 shows the individuals and moving range (I-MR) chart for the waiting time of entrance gate baseline. Figure 6 shows that the process mean and variation of waiting time of entrance gate is not stable. The points of 50, 86.108, 162 and 219 in MR chart and a few ranges in I chart are 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.

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Histogram of waiting time of entrance gate 50

Frequency

40 30 20 10 0 0

100 200 300 400 500 600 Waiting time of entrance gate (second)

700

Figure 4. Waiting time histogram of trucks at the entrance gate

Histogram of waiting time of exit gate 18 16 14 Frequency

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

12 10 8 6 4 2 0 0

400

800

1200

Waiting time of exit gate (second)

1600

Figure 5. Waiting time histogram of trucks at the exit gate

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I-MR chart of waiting time of entrance gate 800 600 400

UCL = 366.5 X = 274.5 LCL = 182.5

200 0 1

25

49

73

97

121

145

169

193

217

Observation

Moving range

Figure 6. I-MR chart for baseline waiting time of entrance gate data

600 400

UCL = 113.0 MR = 34.6 LCL = 0

200 0 1

25

49

73

97

121

145

169

193

217

Observation

I-MR chart of waiting time of exit gate

Individual value

1,600 1,200

1 1 11 5 1

800 400 0

1 1 11 1 1 1 11 11 1 1 1 1 1111 11 1 1 11 1

1

9

17

25

1 11 1 1 11

1

33

Figure 7. Individual and moving range chart for baseline waiting time of exit gate data

11 1 111

1

1 1 1

11 1

11 1 1

11 1 1 1111

UCL = 893 X = 777 LCL = 662

2 22 1 2 2

41 Observation

49

57

65

73

1

200 Moving range

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800

150

UCL = 142.2

100 50

MR = 43.5 22

0 1

9

222

17

LCL = 0 25

33

41 Observation

49

57

65

73

The above I-MR chart indicates that the process mean and variation of waiting time of entrance gate is not stable. The point of 40 in MR chart and the ranges between 1 and 41, also 49 and 78 in I chart are out of control which do not reveal any obvious cause of variation and process mean. Table I represents the DPMO before and after process improvement for main CTQs. Project objective is to reduce the percentage of truck’s waiting time in the entrance

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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 designed based 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.

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4.3 FMEA FMEA is a structured and qualitative analysis of a system or function which identifies potential system failure modes, their causes and the effects on the system operation associated with the failure mode’s 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 CTQs

DPMO

Current (%)

Desired (%)

Current

Desired

60 65.6

99.38 99.38

400,000 344,000

6,210 6,210

Waiting time of entrance gate Waiting time of exit gate

Table I. Current and process performance for CTQs

Port equipment Port operators Equipment defects Working hours Hardware and software

Security guards

EDI implimentation

Accuracy

Weighbridges Service patterns

Crane operators Proficiency

Landside cranes Exhaustion

Weighbridge operators

Truck congestion Exit pattern of trucks Port formalities Drivers Bill of Lading

Arrival pattern of trucks Enter/exit processes

Custom formalities

Traffic signs

Trucks

Cargo owners

Figure 8. Cause and effect diagram (loading/unloading operation of trucks)

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Failure mode

Potential effect Severity Potential cause

Truck Dissatisfaction congestion of customers

10

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Accident

Human error and fatigue

Table II. FMEA for truck congestion at weighing operation step

9

8

Financial penalties Increment of trucks’ waiting time

7

Container dwell time Drivers confusion

5

6

3

Current Occurrence control

Working time of weighbridge operators Activity of weighbridge operators Traffic signs Trucks’ drivers

5

Operators’ accuracy Activity of weighbridge operators Administrative processing Weighbridges malfunction Operators’ accuracy Administrative processing Traffic signs

6

Detection RPN

8

Indirect supervision

8

640

7

Indirect supervision

7

490

6

Video supervision No enough supervision Indirect supervision Indirect supervision

3

162

9

405

7

336

6

240

Indirect supervision PM

8

448

5

150

Indirect supervision Indirect supervision Video supervision

7

84

6

240

3

36

5 8 5 2 8 4

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) of congestion in the landside: . X1 ¼ Working time of weighbridge operators (Risk Priority Number (RPN) ¼ 640): total working time of weighting operation during a working day, X1 ¼ 0 when weighing operation time matches the working time of port. . X2 ¼ Activity of weighbridge operators (RPN ¼ 490): efficient work of operators during a working day, X2 ¼ 0 when weighbridge operators have done their job efficiently. . X3 ¼ Administrative processing (RPN ¼ 448): customs formalities for cargo clearance and terminal formalities for transport documents such as bill of ladings (B/Ls), X3 ¼ 0 when both, the customs and port formalities, are done electronically based on the electronic commerce principles. . X4 ¼ Truck’s driver (RPN ¼ 405): familiarity of drivers with port environment, X4 ¼ 0 when truck’s divers are familiar with port area and its formalities. . X5 ¼ Operator’s accuracy (RPN ¼ 336): accuracy of weighbridge operators in doing their job with no error, X5 ¼ 0 when weighbridge operators are accurate and there is no claim on their work.

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4.4 Improvement phase Improvement phase focuses on reducing the amount of variations found in the CTQ by manipulating the five critical Xs; that is, X1 through X5. The main concept behind this phase in the DMAIC method is that the suggestions are based on the analysis of the cause and effect diagram and the FMEA table. The results of the FMEA suggest that the most relevant potential causes to address are operators’ working time (X1) and their activity (X2). The obtained results imply that there should be changes made to the weighbridge operators process and weighting process aiming to decrease variation in the CTQ. 4.5 Control phase The purpose of the control phase is to make sure that improvements are sustained and reinforced (Antony et al., 2006). In this phase, based on the FMEA, the following necessary improvement and control actions are defined, as shown in Table III. Control and improvement plans are defined based on the results of a workshop among experts of SRPC. As shown in the Table III, suggested plans include operational

2,500

100

2,000

80

1,500

60

1,000

40

500

20

0

(%)

RPNs

3,000

tim e ct of w iv ity eig hb of r w ei idge g A h dm br ope ra in idg ist e o tor s ra tiv per e p ato rs ro Tr ces u s O pe cks ing ' ra to driv rs e ' W ac rs ei cu gh ra T br id raff cy ic ge s m sig n al fu s nc tio n

0

A

W or ki ng

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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 working time (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 truck’s drivers (X4) and operator’s accuracy (X5).

Main roots (Xs) of congestion RPNs Percent Cum %

640 490 448 405 336 162 150 24.3 18.6 17.0 15.4 12.8 6.2 5.7 24.3 42.9 60.0 75.4 88.1 94.3 100.0

Figure 9. Pareto chart of main roots (Xs) of congestion

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Item What is controlled?

Requirements

X1

Working time of weighbridge operators

X2

Activity of weighbridge operators Administrative processing

Three working Adjusting weighing shifts for operation with operators working time of port (24 hour) Good trained On the job training operators

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X3

Table III. Control and improvement plans for the critical factors

X4

Truck’s driver

X5

Operator’s accuracy

Improvement plan

Control methods Frequency Indirect supervision by employer

Direct supervision by employer Clear Using electronic B/Ls Direct documentation/ and more coordination supervision by Electronic between customs and middle and top documentation port managers of port Enough Install more direction Direct direction signs signs in port area supervision by in port area port patrols On the job training Direct Good trained supervision by operators/ reports of employers customers

Weekly

Daily Monthly

Daily Per working shift (three times/day)

solutions on direction sign installation, administrative solutions on training and suggestions to improve cooperation among different authorities such as customs and terminal. Among all suggested plans for Xs, the X3 has long-term solution, i.e. its improvement plan needs a long period of time for accomplishment, because it needs an administrative cooperation between customs and terminal authorities. Thus, due to lack of sufficient time, we have to only control X1, X2, X4 and X5. 4.6 Achieved benefits Number of queuing lines and average waiting time of trucks are regarded as the main parameters of a queuing system. Table IV presents the results of implementing the proposed 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 gathered one month after implementing the proposed solutions for Xs, by Six Sigma team members. As shown in Table IV, accomplishing improvement plans, in the case study and controlling them regularly, caused a sensible reductions in truck congestion in both of the entrance and exit gates weighbridges. 5. Conclusion Six Sigma is an accurate systematic framework for quality improvement and business excellence, which has never been academically used in marine container terminals. This paper proposed a Six Sigma methodology aiming to reduce truck congestion in marine container terminals via smoothing the gate activities, in particular weighting process of trucks carrying import/export/transit containers. The DMAIC method along with the SIPOC chart, cause and effect diagram, and FMEA are used as analyses tools in this research, focusing on managerial operations in the entrance and exit gates of the SRPC as the case study.

Item Weighbridges of entrance gate

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Weighbridges of exit gate

Queuing Max. number (Que.) Ave. (Que.) Waiting Max. time (Sec.) Ave. (Sec.) Queuing Max. number (Que.) Ave. (Que.) Waiting Max. time (Sec.) Ave. (Sec.)

Present model

Proposed plan

Congestions reduction (%)

25

6

76

0.8

91

103.2

86

9.31 727.6 274.5

20.21

93

34

3.5

89

15.56

0.6

96

123.2

93

1667.9 777.3

29.01

97

Working time, activity and accuracy of weighbridge operators, drivers of trucks and administrative processing were the main causes of trucks’ congestion in the SRPC. According to the obtained results, followings should be considered for reducing trucks’ congestion: . There should be more control on the weighbridges’ working time. . The service pattern of weighting operation should be modified and changed to the 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 drivers’ confusion with the processes. . EDI should be implemented in the administrative processing, especially customs formalities and B/Ls. Accomplishing the improvement plans in the case study have caused sensible reductions in transit, entrance gate and exit gate weighbridges. Six Sigma is a statistic based analysis tool, which was imposed following limitations 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 during research. . Control phase is the main limitation of this research, wherein it demands a long period for implementing the obtained results of the study. With regards to the mentioned limitations, it might be a good idea to model the control phase with simulation software packages, such as Arena and Flexsim, and analyse the simulated results with Six Sigma.

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Table IV. Achieved benefits of control and improvement plans

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Parola, F. and Sciomachen, A. (2005), “Intermodal container flows in a port system network: analysis of possible growth via simulation models”, International Journal of Production Economics, Vol. 97, pp. 75-88. Regan, A. and Golob, T. (2000), “Trucking industry perceptions of congestion problems and potential solutions in maritime intermodal operations in California”, Transportation Research: Part A, Vol. 34, pp. 587-605. Steenken, A., Vob, S. and Stahlbock, R (2004), “Container terminal operation and operations research – a classification and literature review”, OR Spectrum, Vol. 26, pp. 3-49. Vis, I. and De Koster, R. (2003), “Transshipment of containers at a container terminal: an overview”, European Journal of Operational Research, Vol. 147, pp. 1-16. Zeng, Q. and Yang, Z. (2009), “Integrating simulation and optimization to schedule loading operations in container terminals”, Computers & Operations Research, Vol. 39, pp. 1935-44. 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 transshipment marine terminals”, Transportation Research: Part E, Vol. 45 No. 5, pp. 771-86. Schroeder, R.G., Linderman, K., Liedtke, C. and Cheo, A.S. (2008), “Six Sigma: definition and underlying theory”, Journal of Operations Management, Vol. 26, pp. 536-56. Tkac, M. and Lyocsa, S. (2009), “On the evaluation of Six Sigma projects”, Quality & Reliability Engineering International, Vol. 26, pp. 115-24. Corresponding author Amir Saeed Nooramin can be contacted at: [email protected]

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