Delphi Forecasting for Shipping Industry and Technology: Performance and Validity

Electronic copy available at: http://ssrn.com/abstract=2239723

Delphi forecasting for shipping industry and technology: Performance and validity

1


Okan Duru, Emrah Bulut, Shigeru Yoshida

Department of Maritime Transportation and Management Engineering,

Istanbul Technical University, Tuzla 34940, Istanbul, Turkey.

&

Department of Maritime Transportation Systems,

Kobe University, Higashinada 658-0022, Kobe, Japan.

Electronic copy available at: http://ssrn.com/abstract=2239723


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Delphi forecasting for shipping industry and technology: Performance and validity†

Okan Durua,*

, Emrah Bulutb, Shigeru Yoshida

b

aDepartment of Maritime Transportation and Management Engineering,

Istanbul Technical University, Tuzla 34940, Istanbul, Turkey.

bDepartment of Maritime Transportation Systems,

Kobe University, Higashinada 658-0022, Kobe, Japan.

Abstract

The aim of this paper is to investigate the performance of the Delphi group consensus

forecasting for the ship industry and technology. Delphi method is the most popular technique

for technology and long term forecasting and it is an aid in decision-making based on the

opinions of experts, which has been in existence for over half a century. The present research

considers its performance and validity particularly in the area of shipping industry. Three

existing applications in this field are reviewed which have some improvements and

drawbacks due to their objectives, the nature of subjects and properness according to the

recent developments of Delphi. These applications highlight how this technique may be

adapted to increase validity in the future studies. Finally, there is a statement of a number of

lessons learnt from the empirical studies, which may contribute to the successful outcome of a

Delphi practise in shipping industry.

Keywords: Technology forecasting; Delphi group decision; Shipping industry.

†An earlier version of this paper is presented at the First Global Conference on Innovation in

Maritime Technology and the Future of Maritime Transportation in Istanbul, Turkey,

November 24-26, 2010.

*Corresponding author. Tel: +81 90 9867 8949; fax: +81 78 431 6259. E-mail address:

[email protected]


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I. Introduction

Long term forecasting is a specific field of future sciences. It is used for various

purposes and applied by statistical and judgmental methods. Long term forecasting is mainly

implemented for technology forecasting task, but it is also frequently used for prediction of

the economic trends. In shipping business, long term forecasting applied by a limited research

and these studies have some typical structures such as the conventional literature. On the other

hand, these studies have some critical outcomes according to their methodology and

predictive accuracy.

The last century demonstrated the importance of technology forecasting in shipping

industry with developments that are thought as imaginary previously, but a new era is faced in

the course of time due to intelligent improvements of shipping industry. The shipping

technologies enhanced performance and effectiveness of seaborne transportation e.g.

invention of containers, combined carriers, increasing cargo handling speed, high

performance propulsion systems. Technologies affected operational and financial aspects of

shipping industry. Shipping markets are also orientated by technology in terms of pricing

services and ships. Forecasting ship technology has a crucial field of research because of such

impacts on shipping business.

Economic forecasting is also one of the important application areas of long term

prediction. Shipping investments and investments which are connected to the industry of

maritime affairs are mostly financed by commercial banks, and these investment projects are

required to be consistent for future fluctuations of the shipping market. Most of the shipping

projects are based on a long term schedule. For example, a merchant ship project is beginning

with acquisition of a second hand ship or signing a new building project, and it concludes by

completion of financial agreement or sale of investment. This schedule is distributed on 5 to

25 years in general according to conditions of the shipping markets. Such a long duration


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brings various operational and financial risks which are including risk of income and

unexpected upswings of several costs. In the banker’s perspective, it is defined as credit risk.

How to establish a long term prediction of economic aspects of shipping business is one of the

critical accounts of investors and lenders for clearing credit risk issue [1].

The recent work suggested some proper methods for econometric analysis and prediction

of the shipping markets [2]. Likewise, the judgmental forecasting is also providing some long

term forecasting methods, and it is applied by many scholars for various prediction purposes.

For long term judgmental forecasting, the Delphi methodology is the most popular approach

in the current literature, and it can be implemented for both technological and economic

visions. As a short term forecasting tool, it can be designed for the adjustment of statistical

extrapolation. For example, Duru et al [3] proposed a fuzzy-Delphi model for judgmental

calibration of the short term forecast in dry bulk freight market and introduced its superiority

by comparing with the statistical benchmark.

Delphi method is particularly accurate when the historical data is limited or the objectives

are completely judgmental. For instance, a technological development is a judgmental

statement and it must be treated by judgmental methods. We may measure technological

improvements by some variables such as average speed of ships, but it is not an explanation

of timing and content of developments. Kahn and Wiener [4] use different types of

forecasting techniques in their studies. Trend line fitting, Scenario planning and Delphi

technique are some of the methods in this seminal study. This research illustrates the Delphi

technique which provides successful forecasts at many of the scientific innovations.

Green, Armstrong and Graefe [5] pointed out some considerable application fields of

Delphi method in a review paper and listed the dry bulk shipping study among the other

globally important items. As our surveys, there are three considerable Delphi studies for

shipping industry in the existent literature. Frankel [6] investigates technology planning


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objective in shipping industry and expresses a Delphi forecasting experiment by Japan

Transport Economics Research Centre (JTERC) (1970). At the same time, Moore and

Pomrehn [7] investigated a technology focused forecasting study by Delphi methodology in

University of California. Furthermore, Ariel [8] tries to perform a long-term forecasting study

by Delphi methodology for mainly economic prediction of shipping business.

The main objective of the present paper is to compare initial expectations and results of

the Delphi applications in shipping business and the research attempts to conceive prominent

points and weaknesses according to the published papers about the methodology and

implementation of Delphi like procedures [9, 10]. Although the number of Delphi studies is

limited in shipping business, the current literature maintains several critical points about the

success of a Delphi study among the all other experiences in different application fields. In

the reference of these major evidences, the Delphi practice in shipping business is reviewed

and discussed.

The structure of this paper is as follows. Section 2 presents the Delphi technique among the

advantages and disadvantages of method. Section 3 presents the empirical studies of JTERC

(1970), Moore and Pomrehn (1971) and Ariel (1989). It also compares some of the forecasted

and actual items as an example. Section 4 discusses performance and validity of the Delphi

technique in shipping industry. Finally, Section 5 concludes the present study and recommends

some research extensions.

II. The Delphi method

Delphi method is used by many empirical studies and frequently suggested by many of the

researchers. The main reasons of popularity of Delphi method are existed by more structured

technique and consensus making approach in particular. Delphi technique is a method

developed at the RAND Corporation in the middle of the last century [10]. It is a qualitative

approach that seeks to use the judgement of experts systematically in arriving at a forecast of


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what future events will be or when they may occur. The approach uses a series of

questionnaires to elicit responses from a panel of experts [11].

Delphi was developed during the 1950s while involved on the US Air Force-sponsored

Project Delphi. The aim of the project was the application of expert opinion to the selection of

an optimal US industrial target system, with a corresponding estimation of the number of

atomic bombs required to reduce munitions output by a prescribed amount. More generally,

the technique is seen as a procedure to "obtain the most reliable consensus of opinion of a

group of experts by a series of intensive questionnaires interspersed with controlled opinion

feedback" [12].

The Delphi method is an approach used in forecasting the likelihood and timing of

future events. The Delphi is one of the limited number of methods in the situations, which

have few historical data or a huge number of factors. The most important pre-requisite for

using the method is that all subjects should be experts in a given aspect of the proposed

objective.

In particular, the structure of the technique is intended to allow access to the positive

attributes of interacting groups (knowledge from a variety of sources, creative synthesis, etc.),

while pre-empting their negative aspects (attributable to social, personal and political

conflicts, etc.). From a practical perspective, the method allows input from a larger number of

participants than could feasibly be included in a group or committee meeting, and from

members who are geographically dispersed.

Delphi is not a procedure intended to challenge statistical or model-based procedures,

against which human judgment is generally shown to be inferior: it is intended for use in

judgment and forecasting situations in which pure model-based statistical methods are not

practical or possible because of the lack of appropriate historical/ economic/ technical data,

and thus where some form of human judgmental input is necessary [13]. Such input needs


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to be used as efficiently as possible, and for this purpose the Delphi technique might

serve a role.

Mainly four key attributes are defined to describe a Delphi procedure [14-16]:

• “Anonymity” which is provided by questionnaires, allows that the group

members can express and revise their judgments without drawbacks of meetings.

The members’ identity is kept privately and social pressures are eliminated.

• “Iteration” improves to revise and consider their judgments according to the

judgments of other group members. The number of iteration is subject to

statistical aggregation and the guidance of Delphi panel moderator.

• “Feedback” is another attribute of Delphi method that provides to evaluate

individual judgments in all iterations. The moderator controls the feedback

procedure and content to obtain higher performance of group consensus.

• The last specification of Delphi is “statistical aggregation”. The statistical

aggregation is averaging of group judgments by simple average or median

methods. In the final iteration, judgments of every participant have an equal

weight and affect outcome of the Delphi session.

Given the problems that traditional groups have with making judgmental predictions,

one would expect that Delphi, with its structured approach, would improve accuracy. Rowe

and Wright [17] found that Delphi improved accuracy over traditional groups. Over all of

the 24 comparisons, Delphi improved accuracy in 71% and harmed it in 12%.

One of the aims of using Delphi is to achieve greater consensus amongst experts.

Empirically, consensus has been determined by measuring the variance in responses of

Delphi participants over rounds, with a reduction in variance being taken to indicate that

greater consensus has been achieved. Results from empirical studies seem to suggest that


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variance reduction is typical, although claims tend to be simply reported unanalyzed [18],

rather than supported by analysis. Indeed, the trend of reduced variance is so typical that

the phenomenon of increased 'consensus', per se, no longer appears to be an issue of

experimental interest. In practice, the coefficient of variation, which is a value of standard

deviation divided to mean of the data, is frequently used for checking variance improvement.

III. The empirical studies for shipping industry forecasting

Forecasting methods are widely used in maritime literature and the majority of them

commonly implement statistical approaches. Nevertheless qualitative processes broaden

the perspectives of forecasting recipe with a judgmentally adjusted strategy. The Delphi

methodology is widespread use in qualitative direction of scientific process to elicit

subjective attributes of research object.

The empirical studies of Delphi for shipping industry forecasting have considerable

outcomes to establish better solutions for the next exercises. The first experimental works

are performed in 1970s by JTERC and University of California. These studies have an

emphatical meaning for understanding technology focused performance of the Delphi in

maritime literature. Subsection 3.1 and 3.2 review these two technology focused study in

terms of technique implementation and accuracy of predictions.

Unlike the technology focused experiments, point specific economic forecasting is a

different scope of futures studies. Strategic foresight ordinarily utilizes point specific

forecasts, variable interval forecast, or timing of an innovation. While the studies of JTERC

and University of California use an innovation timing prediction model, Ariel [19] tries a

point specific forecast of some variables in shipping industry by the Delphi. Depending on

objectives of the present paper, prediction type of experiment denotes one of the differences

as a diagnostic factor. Subsection 3.3 reviews a point specific Delphi forecasting for shipping

industry.


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3.1 The study of Japan Transport Economics Research Centre-1970

Frankel [20] investigates the technological planning and forecasting problem for

maritime transportation which is principally focusing on the studies about technology

forecasting of the shipping and shipbuilding industry. One of the emphasized studies is

performed by Shipbuilders Association of Japan (SAJ) in 1960 and 1965. According to cited

review, this forecasting study performed fairly due to comparisons of actual and predictions.

A structured experiment is also reported which is the study of the Japan Transport Economics

Research Centre (JTERC) in 1970 by the Delphi technique. The objective of the mentioned

Delphi practise was also forecasting of shipping and shipbuilding technology. Most of the

prediction items are found substantially inaccurate by their objective-result comparisons.

Table 1 lists some of the particular technology forecasts of the study.

Table 1. Delphi forecasts in JTERC study.

Objectives Forecast Actual

Completion of the first submersible rescue ship 1979 2006

Completion of technology to prevent disasters due to a large

quantity of flooded oil 1979 NA

Completion of the first lifesaving bridge of automatically

detachable type 1979 NA

Completion of the first ocean-going trimaran ship 1979 NA

Completion of the first oil tankers of one million tons

deadweight 1980 NA

Completion of the first merchant ship with prime mover of

fuel cell type 1983 exp. 2010

Completion of the first ship with batteries for propulsion 1985 exp. 2010

Completion of the first submarine merchant ship 1985 NA

Completion of the first ship built with man-hours of less than

one-third of the one in 1969 1985 NA

Completion of the first ocean-going unmanned merchant ship 1987 Eng.

Notes: NA: Not applicable, because this item is not practically used yet.

Eng.: Unmanned engine rooms are presently in operation. However, a fully unmanned ship is not in

operation yet.

Exp.: Expected to be according to the recent developments.

Source: [7], [21].


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In order to the review of listed predictions, most of the Delphi forecasts do not respond

actual developments. Although a few of them is developed, practically not used, or postponed

for an economically and technically proper time. The performing environment and timing are

also major influences on the Delphi practises. One unique example is prediction of tankership

size trend. Building of one million dwt of oil tanker is inferred in the time of oil shocks of the

20th century. After the 1967 Six Day War, the Suez Canal was closed by Egypt until 5th June

1975. An increasing ship size trend was triggered by the canal closure which induced historic

developments on building of large-scale merchant ships. The local trend of ship size was

interpreted as a long lasting impact. At that time, economic and political psychology and

cyclical influences embodied predictor’s reasoning.

A number of predictions pointed out lagged forecasts which are recently in developing

progress of ship technology. Battery for propulsion and ships with prime mover of fuel cell

are issues of the European Union (EU) nowadays. The critics of environmentalists that

emissions of sulphur dioxide and nitrous oxide from ships are expected to be more than land

based emissions in the European Union by 2020, motivated EU funded projects Zemships

(Zero Emission Ships) and NEW H SHIP (New Hydrogen Ship) to establish technical

circumferences of hydrogen cells and develop the first models of fuel cell ships [22, 23].

The submarine merchant ship is still a dream of technology and it has various difficulties

according to economic operation and the recent submarine technology. However, submarine

rescue ships are used after 2006 which is developed for submarine vessel rescue operations

[24].

The study also consists of predictions for manpower capacity on board. The working

hours of ship’s crew is expected to be one third of 1969 levels. However, when the

technology ensure less working hours for crew, shipping companies look for a manning

reduction and it is also supported by safe manning requirements. As compared to 30 years ago,


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working duration is partly decreased and unmanned engine room is unique development of

new built vessels today. On the other hand, dual purpose accreditation of seafarers is recently

a next step to reduce crewing requirements which is initiated by major liner shipping

companies. Although, the overall working hours and physical load of crew are substantially

declined by the current ship building technology, the decline of individual working time is not

clear and at least it is not one third of 1969 level. The ship’s crew is still working about 12-16

hours a day in an average merchant ship condition.

3.2 The study of Moore and Pomrehn-1971

Moore and Pomrehn [25] investigate the U.S. maritime transportation industry and use

some alternative methods to forecast developments of the industry. The most important

part of this study uses the Delphi forecasting method among wide range future predictions.

The study is carried out with college students who attended Ocean Transport and Logistics

course in University of California.

The items of prediction task include different parts of expertise such as vessel

design, performance, ports and terminals etc. Because of lack of statistics and available

database, a complete inspection of forecast performance is very difficult in most of these

titles. However, some of them are possible to compare with actual developments and to

criticise prediction competency on an objective base. Table 2 shows some of the forecasted

titles and actual situation of them.


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Table 2. Delphi forecasts in Moore & Pomrehn’s study.


500 000 DWT tanker or dry cargo vessel in operation 1986 1976a

1 million DWT cargo ship in operation 2000 NA

LASH (Lighter Aboard Ship) ships in widespread use 1980 NAb

Roll on-Roll off (RO-RO) ships in use 1980 before 1980c

Catamaran cargo liner operational 1985 NA

Economical 50 to 100 knots cargo liners in use 1990 NA

Majority of new cargo liners have nuclear power plants 1985 NA

30 knots average cargo liner speed 1990 NAd

Towed underwater bulk carrier in operation 1985 NA

Satellite navigation and communication in widespread

use 1980 1985-1990

Crew size reduced of 1/3 of present (1970) 1988 NAe

Fully automated engine room developed 1978 1985-1990

All weather operations at sea and in port 1985 NA

Container design standardized 1978 1979f

Notes: a “M/T Jahre Viking” is a 564 763 dwt tanker which is built in 1976.

b Container transportation have been developed rapidly and LASH-Carriers could not success to be

widely operated. c Several records prove that RO-RO ships were operative before 1980.

d Average speed of cargo liners is still lower than 30 knots. Although the container fleet has a higher

speed average about 20-25 knots, for other fleet average speed is less than this level. e Cargo ships have half the crew size of 1970 levels. However, there is no any specific ship size

definition for this item. Today’s an average cargo ship has 30 000 – 50 000 dwt size according to share

of world total dwt capacity and such a tonnage employs about 20 crews on board. On the other hand,

container ships partly reduced crew size about 10 to15 [26, 27]. f ISO 668:1979 [28].

NA; Not applicable, because this item is not existed or practically used yet.

Source: [8], [28], [29].

The study of Moore and Pomrehn [30] was performed with a college student group.

Notwithstanding the Delphi technique particularly requires expert driven predictions;

overall expertise was contradictorily weak in this study. The results of the predictions are


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indicated to fail existence and not operative in most, even in the current ship technology.

Some of the predictions have a timing difference.

A 500 000 dwt tanker or dry cargo ship is expected to be in service about 1986, but

bigger size of vessels are demanded before 1980s. 564 763 dwt tankership, M/T Jahre

Viking, was built in 1976 by Sumitomo Heavy Industries Shipyard of Japan. JTERC expert

group attempts to predict timing of a one million dwt oil tanker in service and prescience of

this Delphi study expected to be actualize in 1980 (median forecast of group). In the

increasing tonnage trend, Japanese expert group who was mainly originated from shipbuilding

industry expected continuity of the trend. In 1975, Suez Canal was opened for maritime

transits and tonnage trends are substantially changed because of oversupply tonnage

(decreasing transport distance induced recession by lesser demand). Simultaneously a deep

recession was recorded in freight markets as well.

LASH (Lighter Aboard Ship)-Carrier System vessels would be expected to be in

widespread use by the transport experts in 1950s. Although standardisation of containers

was in progress, it took a long time to orientate ports and ships for container technology.

The Delphi study is also affected by these developments and LASH-Carrier system is one

of the expected futures of part cargo shipment. In the second half of 20th century,

Container transportation envelops part cargo traffic including LASH type shipments in

mobilisation trend. LASH-Carrier system never had a chance to be used widely.

In technology forecasting, estimating speed or power of equipment is a conventional

prediction strategy which most of the futurists think them designates overall technology

development level. From this point of view, ship speed might be assumed as a leading

indicator of shipping industry development. Unlike the Delphi study of JTERC and Ariel,

Moore and Pomrehn tend to predict speed of ship and timing of probable improvement

levels of it. First attempt is to determine timing of primary high speed cargo liners of 50-


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100 knots level. The second attempt is to define average speed of fleet whether it reaches

to 30 knots level. Nowadays, the economical speed of merchant ship fleet is in the 12-20

knots range. According to diesel engine capacity, ship size, bunker consumption and

bunker prices, higher speed vessels are not inferred feasible yet. Container ship fleet partly

has about 20-25 knots performance.

An interesting expectation is underwater cargo carriers. Whether in JTERC study or

in Moore and Pomrehn’s study, service timing of underwater cargo carrier is similarly

forecasted in about 1985. Submarine vehicles are still not in use for cargo carrying

purposes. The size of submarine vehicles is still limited for a huge size of seaborne trade.

Crew size expectation predicts one third of 1970’s level which signifies

approximately 10-15 crews on board. Crew reduction is mostly successed by fully

automatic engine room implementation. However, the share of tonnage over 25 000 dwt is

83.2% and crew size of such a tonnage is mostly over fifteen [26, 27]. Same study also

concentrates engine room automation objective and predicts that the timing of practical

usage would be at the end of 1970s. Actually fully automated engine rooms are initiated by

around 1985-1990. At that time, crew reduction trend widely affected shipping industry by

the ship technology improvements. In the late 1990s, new generation containerships have

been put in service with extremely crew reductions. These ships are operated with around

12-15 crews. So, the objective of one third of 1970’s crew size is partly accomplished.

Container standardization is completed in 1979 by the reference publication ISO

668 of International Standardization Organisation. The study predicted to be actualizing in

1978 that is very close to original timing.

Catamaran structure and nuclear power are used for different purposes, but it is not

implemented for cargo ships, or it is not found to be feasible. Catamaran hull design is


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frequently used for passenger ships and sportive boats. Nuclear power is first applied for

navy ships and later it is used for some ice breaker ships as well.

3.3 The study of Ariel-1989

Ariel [31] attempts a Delphi application in dry bulk shipping and mainly indicates

economic trends of the dry bulk shipping industry by an expert group. The Delphi exercise

of the study includes three iterations and a consensus scenario of the industry is presented

by value, date and characteristic dimensions. This study uses quantitative variables as

forecasting items for the most part rather than a descriptive expression. Green, Armstrong

and Graefe [32] listed this study in considerable applications of the Delphi method among

the other globally experiments.

Ariel’s study is carried out especially for understanding future directions of shipping fleet

after the deepest slump of 1980s. A reference year is determined as 2000 and future events are

asked to expert group particularly for economic forecasting. Table 3 gives some of the Delphi

forecasts from the study with forecast and actual results. Some of the forecasting items are not

included in this paper because of the time of forecast actualization or lack of proper data for

assessment. The second column of table demonstrates forecasted value as median forecast of

the expert group. The actual values of items are provided from various shipping periodicals

and reports [33, 34].


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Table 3. Delphi forecasts in Ariel’s study.


World economic recovery 1992 1990-92

Revival of dry bulk shipping to 1973 levels 1990 1990a

Total deadweight capacity of the dry bulk fleet in 1990 200 mills. 208.8 mills.



The proportion of the dry bulk fleet completely laid-up

in 1990 3.5% 2.2%


in 1995 3% 2.1%


in 2000 2.7% 1.6%

Supply-demand gap ratio of the dry bulk fleet in 1990 15% 15.4%

Supply-demand gap ratio of the dry bulk fleet in 1995 10% 10.6%

Supply-demand gap ratio of the dry bulk fleet in 2000 10% 11%

Annual scrapping of 5% 1990 1998

50% of merchant ships above 5 000 dwt equipped with

electronic mail 1995 1995-2000

Note: a Although, the levels of 1990’s decline did not exactly reach to the levels of 1973’s recovery, recession

affected dry bulk freight markets and time charter base prices decreased to about 1973’s boosting prices

[35].

Source: [9], [33], [34].

Two significant differences are recognised in this application:

• The study is performed with an expert group who is originated from a wide range

society of the industry that including different branches of expertise. Most of the

experts are constituted by on the job professionals.


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• Predictions of experts are mostly established on the forecasting of the value of a

data according to predetermined timing. The prediction horizon is defined by

facilitator and subjects are asked to estimate value of a quantitative variable.

Rather than a descriptive expression of an innovation, the volume of a variable is

selected to be predicted in a specific time span [36].

World economic recovery, laid-up ratio of dry bulk fleet, supply-demand gap ratio and

some other objectives are forecasted with higher accuracy. Scrapping of dry bulk carriers is

expected to be 5% of the fleet in 1990 that is shortly after the recession of 1987-88. At that

time, experts were in a slump market which may have been affected their expectations.

Scrapping of 5% of fleet is actually recorded in 1998 when a recession period existed before

the recovery of 2001 in dry bulk freight market. Fig. 1 shows the development of dry bulk

fleet and laid-up tonnage in period of 1980-2007.

Dry Bulk Fleet Capacity in kdwt

1990; 208,8001995; 232,300

2000; 255,542

2005; 308,848

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

1980 1985 1990 1995 2000 2005

Year

Dry Bulk Fleet / kdwt

0

5,000

10,000

15,000

20,000

25,000

Dry Bulk Fleet Laid-Up Tonnage in kdwt

Laid-Up Tonnage / kdwt

Fig. 1. The dry bulk shipping fleet capacity and laid-up tonnage [33, 34].

Moreover, the forecasts of supply-demand gap ratio provided very high accuracy and it could

be analysed to predict freight market in econometric studies.


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IV. Performance and validity of Delphi forecast in shipping

JTERC [37], Moore and Pomrehn [38] and Ariel [39]’s studies investigated Delphi

forecasting for marine technology and shipping economics. Three works have some

similarities even same forecasting items. JTERC and Moore & Pomrehn’s studies mostly

concentrated to technological forecasting and ship design aspects. On the other hand, Ariel’s

Delphi study focused on shipping economic which mainly consists of items that are fleet

capacity, utilization and demand aspects of world seaborne trade.

In this circumstances, the Delphi studies in this paper, separated to two conceptual

types. Objective of Delphi study can be focused on technological innovations or economical

developments. By this classification, the studies of JTERC, and Moore and Pomrehn are

based on ship and port technology developments. On the other hand, the study of Ariel is

based on developments in shipping economics.

Moreover, these studies are subdivided into objective strategy. Objectives can be

introduced by a single innovative idea and the timing of actualization will be asked to subjects.

Another option is that objective is based on a significant time and value of a specific variable

is asked to be predicted. By this classification, both technology focused studies are

constructed on timing prediction. The study of Ariel is based on a point specific forecasting of

a variable. Fig. 2 depicts this classification.


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The studies of

JTERC (1970)

Moore & Pomrehn (1970)

The study of

Ariel (1989)

Not existed

in the current literature

Not existed

in the current literature

Technology focused Economy focused

Timing of objective

Value in spesific time

Fig. 2. The structure of Delphi study strategies.

As it is indicated in Fig. 2, economical long term forecasting is not proposed by

objective timing method in general, or technological long term forecasting is not based on

values of variables in a specific time. Ariel [39] partly applied objective timing method for

economical variables such as economic recovery, 5% scrapping level. For example, the

average speed of dry bulk fleet can be asked for a specific time, or the time of turning points

in freight markets can be predicted. Both strategies have limited examples.

The inspection of Delphi performances clearly exhibits higher capabilities of statistical

point forecasts rather than a technology estimation study. Technology forecasting task

requires more engineering skills and wide range understanding of shipping industry. In

practical usage of Delphi, expertise of panellists is one of the unique specifications for higher

performance. Rowe and Wright [40] investigate many different Delphi applications and

particularly suggest high expertise groups. Moore and Pomrehn [38] distinctly used college


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students as predictor of shipping industry. Accordingly the prediction performance is

suspicious and including various incompetencies. Several predictions are still not applied or

practically used in general.

Ariel [39] uses highly expert group to forecast over twenty items and most of the

participants are defined from practical labour area. Forecasting accuracy of quantitative

results in this study is measured by the conventional Root Mean Square Error (RMSE) metric.

The RMSE metric gives an average deviation interval, and increases effects of larger errors

by squares of them. Eq. (1) indicates the RMSE formulation. Xi is the prediction and Yi is

actual result. The number of prediction is indicated as n.

( )2

1

n

i iiX Y

RMSEn

=−

=∑

(i=1,…, n) (eq. 1)

The RMSE results of dry bulk fleet capacity indicated 28 Million dwt error rate and it means

about 12% of actual statistics. On the other hand, the results of laid-up tonnage ratio and

supply-demand gap ratio predictions provide 1% deviation on percentage expectations.

V. Conclusions and outlines for future research

The Delphi method is one of the alternatives for judgmental forecasting. Many applications

try to improve higher accuracy among the expert groups by the Delphi panel forecasts.

However, recent studies indicate performance differences into different execution strategies.

This paper investigated performance and validity of Delphi in maritime transportation and

finds some significant particulars to be able to adjust in the next practises.

Expertise of panellist is observed one of the most effective attribute for forecasting

success. Moore & Pomrehn [38] tried to perform a Delphi round with college students and

results have various incompleteness of technological development. Nevertheless Ariel [39]

improved higher forecasting performance by on the job, practicing expert group. However,

perspective of these two studies is different.


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The objectives of the task are other drivers of Delphi panel as a performance provider.

Fully technology focused tasks are found less performance and invalid for practically usage.

Results are usually out of real development pattern of the industry and can not be interpret as

valid predictions. Quantitative variable or point specific forecasts are found more capable to

catch actual fulfilment. Quantitative data is able to be compiled by historical pattern and

recent trends. Technological items are rarely predicted on time and as rational. Prediction of a

variable in a specific time ensures higher capability.

Ono and Wedermeyer [40] reported that a Delphi panel produced forecasts that were

over 50% accurate, and used this as 'evidence' that the technique is somehow a valid

predictor of the future. However, the validity of the technique, in this sense, will depend as

much on the nature of the panelists and the task as on the technique itself.

Judgmental forecasting is a new research area in shipping industry except a number

of former studies and gaps still exist how to improve valid predictions [41]. As a judgmental

forecasting tool, the Delphi provides a consensus building methodology for shipping industry

practise. Although recent theoretical and empirical developments have improved our

understanding of the role of quantitative methods in shipping market forecast, subjective

factors of the industry is an existent field of future research [42]. The Delphi method can be

extended among the shipping market forecasts in short term as well. The results of long term

forecasting by Delphi indicated several errors, and it can be proper for short term experiences.

Recent maritime research has limited research that criticises accuracy of judgmental forecasts

and it is one of the extending fields to develop a judgment sensitive forecasting for shipping

industry.


22

References and notes

1. Harwood, S., Shipping Finance, Euromoney Institutional Investor Plc, London, pp. 70-

92 (2006).

2. Batchelor, R., Alizadeh, A. and Visvikis, I., 2007, “Forecasting spot and forward prices

in the international freight market”, International Journal of Forecasting, Vol. 23, No.1,

pp. 101-114 (2007).

3. Duru, O., Bulut, E. and Yoshida, S., “A fuzzy extended DELPHI method for adjustment

of statistical time series prediction: An empirical study on dry bulk freight market case”,

Expert Systems with Applications, doi:10.1016/j.eswa.2011.07.082 (2011).

4. Kahn, H. and Wiener, A.J., The year 2000-A framework for speculation on the next

thirty-three years, McMillan, New York, (1967).

5. Green, K., Armstrong, J.S. and Graefe, A., “Methods to elicit forecasts from groups:

Delphi and prediction markets compared”, Foresight: International Journal of Applied

Forecasting, Vol. 8, pp. 17-20 (2007).

6. Frankel, E.G., Ocean Transportation, MIT Press, Cambridge (1973).

7. Moore, C.G. and Pomrehn, H.P., “The Technological forecast of marine transportation

systems 1970 to 2000”, Technological Forecasting and Social Change, Vol. 3, pp. 99-

135 (1971).

8. Ariel, A., “Delphi forecast of the dry bulk shipping industry in the year 2000”, Maritime

Policy and Management, Vol. 16, No.4, pp. 305-336 (1989).

9. Rowe, G. and Wright, G., “The Delphi technique as a forecasting tool: issues and

analysis”, International Journal of Forecasting, Vol. 15, pp. 353-375 (1999).

10. Dalkey, N. and Helmer, O., “An experimental application of the Delphi method to the

use of experts”, Management Science, Vol. 9, pp. 458-474 (1963).


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11. Makridakis, S., Wheelwright, S.C. and Hyndman, R.J., Forecasting Methods and

Applications, John Wiley and Sons, New York, pp. 482-511 (1998).

12. Dalkey, N. and Helmer, O. (1963), supra, note 10.

13. Wright, G., Lawrence, M.J. and Collopy, F., “The role and validity of judgment in

forecasting”, International Journal of Forecasting, Vol. 12, pp. 1-8 (1996).

14. Rowe, G. and Wright, G., (1999), supra, note 9.

15. Stewart, T.R., “The Delphi technique and judgmental forecasting”, Climatic Change,

Vol. 11, pp. 97-113 (1987).

16. Linstone, H.A. and Turoff, M., The Delphi method: techniques and applications,

Addison-Wesley, London (1975).

17. Rowe, G. and Wright, G., “Expert opinions in forecasting: the role of the Delphi

technique”, In: Principles of Forecasting, edited by J.S. Armstrong, Kluwer Academic

Publishers, Boston, pp. 125-144 (2001).

18. Dalkey, N. and Helmer, O. (1963), supra, note 10.

19. Ariel, A., (1989), supra, note 8.

20. Frankel, E.G., (1973), supra, note 6.

21. Actual results of forecast objectives were compiled from various maritime sources and

personal communication with several experts.

22. http://www.zemships.eu/

23. https://www.hfpeurope.org/

24. http://www.naval-technology.com/projects/lr5/

25. Moore, C.G. and Pomrehn, H.P., (1971), supra, note 7.

26. Committee on the effect of smaller crews on Maritime Safety Marine Board

Commission on Engineering and Technical Systems National Research Council, 1990,

Crew Size and Maritime Safety (Washington, D.C.: National Academy Press).


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27. Institute of Shipping Economics and Logistics, Shipping Statistics Yearbook, 2007

(Bremen: Institute of Shipping Economics and Logistics).

28. http//www.iso.org, ISO Reference publication ISO 668: Series 1 freight containers-

Classification, external dimensions and ratings, 1979.

29. Actual results of forecast objectives were compiled from various maritime sources and

personal communication with several experts.



32. Green, K., Armstrong, J.S. and Graefe, A., (2007), supra, note 5.

33. Fearnleys Monthly and quarterly dry bulk reports (various issue).

34. Lloyd’s Shipping Economists, various issues of magazine.

35. Institute of Shipping Economics and Logistics, Shipping Statistics Yearbook, 1991

(Bremen: Institute of Shipping Economics and Logistics).

36. The strategy difference can be easily recognized by the comparison of tables.

37. Frankel, E.G., (1973), supra, note 6.



40. Ono, R. and Wedermeyer, D.J., “Assessing the validity of the Delphi technique”,

Futures, Vol. 26, pp. 289-304 (1994).

41. Duru, O. and Yoshida, S., “Composite forecast: a new approach for forecasting shipping

markets”, Proceedings of the International Association of Maritime Economists

Conference 2008, Dalian, China (2008).

42. Duru, O. and Yoshida, S., “Market Psychology”, Lloyd’s Shipping Economists, August

Issue, pp. 30-31 (2008).

Delphi Forecasting for Shipping Industry and Technology: Performance and Validity

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