(KPIs): “Quality Function Deployment” - SSRN

Shipping performance assessment and the role of key performance indicators (KPIs): “Quality Function Deployment” for transforming shipowner’s expectation* Okan Duru a†, Emrah Bulutb, Sheng Teng Huangc, Shigeru Yoshidad a

Department of Maritime Transportation and Management Engineering, Istanbul Technical University, Istanbul, Turkey. E-mail: [email protected] b Department of Maritime Logistics, Kobe University, Kobe, Japan. E-mail: [email protected] c Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung 20224, Taiwan, R.O.C. E-mail: [email protected] d Department of Maritime Logistics, Kobe University, Kobe, Japan. E-mail: [email protected]

Abstract The aim of this paper is to investigate the role of key performance indicators (KPIs) in third party ship management and their contribution to the Shipping performance index (SPI). This research is mainly motivated from the SPI initiative which is established by the InterManager for the assessment of the shipping performance of the third party ship management companies and to elicit quality service providers and the frontrunner companies in the maritime industry. The SPI is an unweighted average of KPI scores which is calculated from numerical outcomes of several performance indicators. However, the degree of importance and contribution of a specific performance indicator for shipowner’s satisfaction is not taken into consideration. Therefore, a theoretical gap exists on linking the shipowner’s expectations with the technical and numerical measures (i.e. KPIs). This paper proposes a preliminary process to establish the priorities of the KPIs by utilising quality function deployment (QFD). The empirical results indicate the unweighted average method does not represent ship-owners’ perception and the priority of KPIs should be revalued. Key Words: Shipping Performance Index (SPI), Key Performance Indicator (KPI), Service Quality, Third Party Ship Management. JEL Classifications: C44, R41, C02, C54, D81, L25, L21, L91, M31.

* †

This paper is presented at the Conference of International Association of Maritime Economists, Taipei, 2012. Corresponding Author’s Full Address: Okan Duru, Department of Liberal Arts, Istanbul Technical University, Manastir Yolu, Tuzla, Istanbul, Turkey. Email: [email protected] Web: http://web.itu.edu.tr/duruok/

1. INTRODUCTION According to 2008 statistics, 15% of world merchant fleet (over 500 GRT) is managed by third party ship managers and the third party ship management (3PSM) market is extending its business by around 2% annual growth rate (Giorgi, 2008; Drewry Shipping Consultants, 2006). In addition, an increasing number of shipping companies provide the 3PSM service and the competitiveness increases in the market. The management and operation of ship consist of variety of activities including maintenance, crew management, quality management, safety system supervision, fuel efficiency measures, integrated logistics assistance, management of property, procurement and inventory control. Generally speaking, ship management companies recommend a business solution for ship owners to both relieve themselves high daily operation cost and benefit from the financial advantage of economies of scales. Sletmo (1989) addressed the ship management companies can be seen as ‘Shipping’s fourth wave’ to cause organisational reform of global nature. Strong competition across many segments in maritime business points out that the pursing higher level of efficiency and how to stay competitive have become the urgent strategy for ship management companies to gain the advantage in the global market. Since ship management industry is service-oriented, providing the high quality service should be considered as an important intangible resource to gain the potential customer and retain existing customers. Although measuring the service quality is quite difficult, many scholars have explored ship-owners’ viewpoints with regard to the attributes provided by ship management companies and their importance to the choice of ship managers as a strategic plan for developing the service quality. Panayides and Gray (1999) apply the relationship market theory in professional ship management companies. The research shows that the importance of building long term client relationships will assure the client retention, transaction cost reduction, differentiation and competiveness improvement. Mitroussi (2003) investigates the four important factors that influence ship-owner to utilise the third party ship management service. These factors include company size, company type, company age and technological change. Mitroussi (2004) also compares the ship management sector of two traditional maritime centres, Greece and the UK, which is carried out through ship-owner’s viewpoint on several key issues. Technical and crew management are the significant outsourced functions of a ship-owner, while the selection of business matters, flag of registry and maintenance remains ship owner’s accountability. Cariou and Wolff (2011) uses information from Lloyd's register Fairplay 2010 about 45,800 vessels that are owned by 9,580 different owners to explore the level of outsourcing in shipping and to classify key attributes influencing the possibility of outsourcing. The results point out that ship-owner’s choices for outsource is clarified by the vessels in survey (type, size and age) and features of the owner (fleet size, country of registration). Moreover, Greek ship-owners have a lower tendency to outsource their vessels due to particular country effect among six largest owner-registered countries (including Japan, Germany, China, USA and Norway). Although the existing literature investigated the 3PSM by using surveys and statistical data, the service quality assessment is more a technical matter and it is expected to be considered by a quantitative approach. In the last few years, the use of Key Performance Indicators (KPIs) is a popular and trending practice in the business. The ship managers’ association named Inter-Manager provided a basis to establish the integrated Ship Performance Indicators (SPIs) for assessment of the service quality and performance of ship managers. While the SPI is a simple average of the related KPIs, the method assumes that each KPI contributes to the SPI in an equal degree (Also see Table 2 for list of KPIs). For example, flawless port state control and health & safety deficiencies have same priority for final outcome. However, it is clear that the port state controls also cover health & safety

standards. Therefore, port state control is more holistic result than the health & safety deficiencies which may cause different priorities between these items. A ship-owner may prefer a ship manager with a complete success on port state controls rather than a part of it. Risk-averse investors will be particularly sensitive about such differences. However, there are two critical questions. The first one is how to define the related priorities of KPIs, and the second is how to make it robust among different ship-owner perspectives. This paper deals in particular with the first question by utilising QFD approach and a unique resolution for the second debate is the assignment of priorities for every ship-owner in the same manner. Therefore, ship-owners may have an option of either performing a specific priority regime or to retaining it as is (simple average). Service quality assessment has a long history and vast numbers of studies are published in the existing literature. Among these contributions, Quality Function Deployment (QFD) has its particular advantages and it is frequently applied for many empirical works including ship investment decision (Duru et al, 2012). The QFD method is first developed for the service quality of Mitsubishi Kobe shipyard for improving the ship design and ship production process (Akao & Ohfuji, 1989). After its presence, many papers applied the QFD for many different contexts. The QFD method supports the decision maker for collecting customer requirements and for indicating their interactions with the technical content. The major improvement of the QFD approach is to transforming customer needs into practically meaningful and objective measures by an expert aided correlation matrix named House of Quality (HoQ). The HoQ matrix is used for visualisation of both customer requirements and technical measures with their corresponding priority degrees. Since the QFD method is a unique tool for transforming customer needs into the technical measures, the SPI framework can be improved by the analysis of KPIs through the ship-owner’s expectations for the 3PSM service. This paper presents a pilot study for collecting the requirements of a sample of subjects in ship-owning business and introduces the HoQ matrix to derive the contribution of a KPI on the satisfaction of a requirement of ship-owner. The vast majority of KPIs for the third party ship management is already published by Intermanager as well (www.shipping-kpi.org). The empirical work is designed for collecting responses from a group of ship-owner’s representatives as a sample and the priority degrees of the KPIs are presented. For the classification and the assignment of priorities for ship-owner’s requirements, a multi-criteria decision making (MCDM) method, the fuzzy-AHP (FAHP) method, is implemented with the decision maker prioritisation (Bulut, Duru, Keçeci, & Yoshida, 2012). In the second step, the HoQ correlation matrix is assigned by expert consultation including a number of executives in the ship management and scholars in shipping research. 2. METHODOLOGY The proposed approach is designed to assign the global priority degrees (overall) and the local priority degrees (within SPI) of KPIs by investigation of relationship degrees between ship-owners’ requirements and the KPIs (Table 1). Figure 1 illustrates the flowchart for the intended process.

Table 1. List of SPIs (1,2,...), KPIs (a,b,...) and PIs (i,ii,...). 1. Health and Safety Management and Performance a. Flawless Port state control performance i. Number of PSC inspections resulting in zero deficiencies ii. Number of PSC inspections b. Lost Time Injury Frequency i. Number of fatalities due to injuries ii. Number of lost workday cases iii. Number of permanent total disabilities (PTD) iv. Number of permanent partial disabilities v. Total exposure hours c. Health and Safety deficiencies i. Number of health and safety related deficiencies ii. Number of recorded external inspections d. Lost Time Sickness Frequency i. Number of cases where a crew member is sick for more than 24 hours ii. Number of fatalities due to sickness iii. Total exposure hours e. Passenger Injury Ratio i. Number of passengers injured ii. Passenger exposure hours 2. HR Management Performance a. Crew disciplinary frequency i. Number of absconded crew ii. Number of charges of criminal offences iii. Number of cases where drugs or alcohol is abused iv. Number of dismissed crew v. Number of logged warnings vi. Total exposure hours b. Crew planning i. Number of crew not relieved on time ii. Number of violation of rest hours c. HR deficiencies i. Number of HR related deficiencies ii. Number of recorded external inspections d. Cadets per vessel i. Number of cadets under training with the ship manager ii. Number of vessels under technical management (DOC) e. Officer retention rate i. Number of officer terminations from whatever cause ii. Number of unavoidable officer terminations iii. Number of beneficial officer terminations iv. Average number of officers employed f. Officers experience rate i. Number of officer experience points ii. Number of officers onboard g. Training days per officer i. Number of officer trainee man days ii. Number of officer days onboard all vessels under technical management (DOC) 3. Environmental Performance a. Releases of substances as def by MARPOL Annex 1‐‐6

Number of releases of substances covered by MARPOL, to the environment ii. Number of severe spills of bulk liquid b. Ballast water management violations i. Number of ballast water management violations c. Contained spills i. Number of contained spills of bulk liquid d. Environmental deficiencies i. Number of environmental related deficiencies ii. Number of recorded external inspections 4. Navigational Safety Performance (Nav) a. Navigational deficiencies i. Number of navigational related deficiencies ii. Number of recorded external inspections b. Navigational incidents i. Number of collisions ii. Number of allisions iii. Number of groundings 5. Operational Performance a. Budget performance i. Last year’s running cost budget ii. Last year’s actual running costs and accruals iii. Last year’s AAE (Additional Authorized Expenses) b. Drydocking planning performance i. Agreed drydocking duration ii. Actual drydocking duration iii. Agreed drydocking costs iv. Actual drydocking costs c. Cargo related incidents i. Number of cargo related incidents d. Operational deficiencies i. Number of operational related deficiencies ii. Number of recorded external inspections e. Passenger injury ratio i. Number of passengers injured ii. Passenger exposure hours f. Port state control detention i. Number of PSC inspections resulting in a detention g. Vessel availability i. Actual unavailability ii. Planned unavailability h. Vetting deficiencies i. Number of vetting deficiencies ii. Number of vetting inspections 6. Security Performance a. Flawless Port State Control performance i. Number of PSC inspections resulting in zero deficiencies ii. Number of PSC inspections b. Security deficiencies i. Number of security related deficiencies ii. Number of recorded external inspections 7. Technical Performance (Tech) a. Condition of class i. Number of conditions of class b. Failure of critical equipment and systems Number of failures of critical equipment and system i.

Collecting responses for defining shipowners’ requirements from ship manager (preliminary survey with professionals in ship-owning business)

Criteria assessment based on MCDM assumptions and principles (e.g. independence, doublecounting error)

Collecting responses for pairwise assessment – FAHP process Secondary sruvey for defining the importance of requirements

Priority calculation based on FAHP

Consistency Control

framework SPIs and their corresponding KPIs

CCI Loop(Section 2.2.1) QFD based assessment for Global and Local priority

Figure 1. The process for assignment of Global and Local priorities of KPIs. In the first step, the ship-owners’ requirements are investigated among the related literature (Mitroussi, 2004; Panayides & Cullinane, 2002) and also by interviews with the business practitioners in the industry. For example, Panayides and Cullinane (2002) investigate the perception of ship-owners for ship manager selection and a number of major drivers of the selection are addressed. Technical ability, experience, specialization and price are some of the outstanding factors. This study looks for trade-offs between ship managers while dealing with the direct requirements of ship-owners. For instance, experience is a particular dimension of the company which contributes to several requirements such as improving the hull condition, competence to international legal framework etc. Therefore, experience is an indirect dimension rather than the fundamental demand. An experienced ship manager is expected to be superior to handle several issues and disputes seamlessly while the requirement is actually robustness and security of shipping business. Under these considerations, a preliminary survey is designed to elicit the major requirements of ship-owners from ship management services. The preliminary survey is composed of three main questions: The level of confidence for provided response (e.g. a subject is 80% confident for his contribution), list of requirements from ship management service (a number of options are provided based on the literature) and the importance level of these indications3. A group of practitioners in ship-owning business are asked for survey participation. Subjects are 3

The importance level is asked as a preliminary information and for selection of major requirements. The priority degrees of requirements are definitely calculated through fuzzy extended AHP process for predetermined requirements.

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selected from managerial titles (e.g. member of board of directors, operation managers) and responses collected by either electronic mails or interviews (by phone). Twelve responses are received for the proposed pilot study and the collected data is investigated based on the confidence level and the importance levels. These responses are from ship-owning companies with different fleet size and vessel types such as large tanker fleet (over 1,000k DWT total fleet) or medium size dry bulk fleet (approx. 400k-800k DWT total fleet). Since this study deals with the proposed method, the sample size is quite enough to illustrate intended framework. As it is previously discussed, large sample size does not ensure its functionality for a specific ship-owner. In business practice, every customer (i.e. ship-owner) will define their own priorities based on business perception and technical precision. This study is designed as a pilot work, our purpose is to test whether the simple average of KPIs is suitable for business practice or ship-owners have different perceptions. Indications are normalised by using confidence levels and the aggregated importance levels are calculated by the sum of normalised indications. Finally, similar indications are grouped into main titles. The remaining five major requirements are defined as follows: • Cheaper Service (CS) • Competence with international legal framework (IL) • Business network & Cost efficiency(BN) • Ensuring the good condition of hull (structural stability & competence) (GH) • Quality Manning (QM) In the second step, the suitability of these indications is criticised based on the business practice and the theoretical assumptions of multi-criteria decision making (MCDM) methods used in this study. One of the main problems with MCDM studies is the maintenance of the independence principle of the criteria. In many examples, most of the criteria are somewhat depended and the assessment of the level of their dependence can be a problematic task. In the initial process, we already eliminated several items such as experience because of its indirect nature. However, the existing items still have indirect relationships at some degree and the direct relationships should be stated clearly to rule out possible double counting errors or dependency phenomenon. Since the ship is a mobile asset which can easily move between different regional markets, the freight rates have generality among the global shipping market and it does not extremely change according to the employed region. However, it is expected that a greater business network contributes to the superior negotiation nature and to gain higher revenues. From this perspective, ship-owners ask for a cheaper ship management service while providing extended business network with the major carriers and other competitors. The preliminary perspective looks for higher revenues (i.e. higher freight rates), but the prices in the market is usually a compromised result of whole industry rather than a specific region and company. Therefore, the superior revenues are based on strong business relationship and loyalty for the most part. The business network also contributes to the future of the ship since it is recognised by a number of superior customers (charterers). The cheaper price (service fees) and business network are selected under the considerations above and these factors are more of a fiscal matter. On the other hand, the competence with the good condition of hull and quality manning are more of a technical issue. One may think that information spillovers exist between these items since the good condition of hull is secured by the international legal framework (i.e. IMO regulations) at some degree. However, the term, “good condition of hull”, does not refer to the minimum requirements of related legislation, it deals with the additional performance to secure the structural and functional superiority of ship which is based on the technical management skills

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at most and particularly contributes to the sale price of the asset4. Quality manning is partly depended to this topic since the seafarers are responsible for a number of tasks for this purpose. The difference originated from the fact that the role of technical representatives on the motivation and follow-up of seafarers. On the other hand, quality manning has another distinction based on the agency role of seafarers (particularly senior officers). While these items have a weak form of dependence, none of them guarantees another. The competence to international regulations should also be considered under the principles discussed above. The priority degrees of ship-owners’ requirements are investigated by pairwise comparison technique. As a weighted linear additive model, analytic hierarchy process (AHP), is frequently used in decision making problems and it is rated as one of the most useful method for transport problems (Tsamboulas, Yiotis, & Panou, 1999). Rather than the classical form, fuzzy set approach improves the uncertainty problem which is caused by weak dependence and crisp scales. Fuzzy AHP (FAHP) framework is used to collect pairwise comparison responses and to perform further assessments. Once the priorities of requirements are defined, then the HoQ matrix is used to transform requirements into the practical factors, KPIs. The following sections will introduce the methods for the intended research. 2.1 The quality function deployment The QFD framework is designed to translate the requirements of customers into technical measures (TMs) by utilising the House of Quality (HoQ) matrix. The priority degree of a technical measure can be derived by the sum product of the relative weight of a customer requirement and its corresponding relationship degree with intended technical measures. A normalised value of the result will present the relative weight (priority degree) of technical measure for satisfying the customer. The traditional HoQ matrix is composed of seven major parts including the customer requirements (CRs), the priority degree of requirements, technical measures (TMs), the correlation matrix (between TMs), relationship matrix (between TMs and CRs), sum products of priority degrees and relationship degrees, wj, and finally the priority degree of TMs, wnj (See Figure 2). The correlation matrix is particularly useful for developing strategies to improve a technical measure. In some cases, a technical measure has a positive or negative correlation and an improvement may contribute to another or deteriorate it. For assessment of the balance of improvements, correlation matrix indicates such interactions. However, this paper deals with the priority degree of TMs, wnj, and the correlation matrix of TMs is out of scope of this paper. In the present study, CRs are derived from a preliminary survey with professionals in ship-owning business. The priority degrees, di, of CRs are based on the FAHP process. TMs are based on the KPIs of InterManager SPI study. The priority degrees, wj, of TMs will define the importance degree of each KPI among the SPIs.

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In the sale and purchase market, a potential buyer usually employs a representative surveyor to elicit technical condition and performance of the asset. Better structural condition is a superior feature which may ensure higher sale price or a prompt deal in the market.

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Correlation Matrix

The priority degree of requirements di

Customer Requirements (CRs)

Technical Measures (TMs)

Relationship Matrix Rij

Sum Products wj Priority degree of TMs

wnj

Figure 2. House of Quality matrix. The numerical process for assignment of priority degree of TMs is as follows: Let m customer requirements indicated by CRi, (i = 1,2,..., m) and n technical measures indicated by TMi (i = 1, 2,..., n). Let di (i = 1, 2,..., m) be the priority degree of the ith CRi among the whole set of CRs, whereas wj (j= 1, 2,..., n) denoting the relative weight of importance of the jth TM, is determined from the relationship between CRs and TMs. Let R be the relationship matrix between CRs and TMs, the element Rij indicates the level of impact of the jth TM on satisfaction of the ith CR. The value of Rij is assigned by an indicator value of 9 (Strong relationship, “■”), 5 (Moderate relationship, “▲”), 1 (Low relationship, “●”) or 0 (No relationship, “Ø”). The sum product of the priority degree, di of the ith CRi and Rij is calculated as follows m

w j = ∑ d i Rij , j=1,2,…, n

(eq. 1)

i =1

wnj is the normalised value of wj which indicates the priority degree of the jth TM for customer satisfaction. The priority degree of the CRs, di, is defined by an initial FAHP process through the pairwise comparison survey. The following section discusses the FAHP method under the synthetic extent analysis. 2.2. Fuzzy Analytic Hierarchy Process Analytic hierarchy process (AHP) (Saaty, 1980) is one of the widely used method to evaluate complex multiple alternatives, but it is criticised because of not fully reflect the decision maker’s knowledge and thought (Buckley, 1985; Van Laarhoven & Pedrycz, 1983). Buckley (1985), therefore, proposed fuzzy-AHP (FAHP) method to overcome vagueness and

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ambiguity on multi-criteria decision problem. After that, many scholars improve the FAHP method by using a new approach (Buckley, 1985; Bulut, et al., 2012; Chang, 1996; Huang, Chu, & Chiang, 2008; Mikhailov, 2003; Mikhailov & Tsvetinov, 2004; Van Laarhoven & Pedrycz, 1983; Xu, 2000). In this study, Chang’s (1996) synthetic extent analysis approach is applied for the process of FAHP by using six different fuzzy linguistic terms (Table 2) to display the pairwise comparison (judgements) of decision makers (Fig. 3). Table 2. Transformation for TFNs membership functions. Fuzzy number

Linguistic scales

Membership function

Ã1 Ã2 Ã3 Ã4

Equally important (1,1,1) Slightly important (1,1,3) Moderately important (1,3,5) More important (3,5,7)

1,1,1) (1/3,1,1) (1/5,1/3,1) (1/7,1/5,1/3)

Ã5 Ã6

Strongly important Extremely important

(1/9,1/7,1/5) (1/9,1/9,1/7)

(5,7,9) (7,9,9)

Reciprocal

µ A% ( x ) A%1 A% 2

A%3

A% 4

A%5

A% 6

1

1

3

5

7

9

x

Figure 3. Fuzzy numbers in the linguistic variable set. Triangular fuzzy number à in R, indicated by (l,m,u), is applied and its definition is as follows; Definition 1: A fuzzy set à in a universe of discourse R is characterised by a membership function µ A% ( x ) which associates with each element x in R is a real number in the interval [0, 1]. The function value µ A% ( x ) is termed the grade of membership of x in Ã. Definition 2: A fuzzy number is a fuzzy subset in the universe of discourse R that is both convex and normal. Definition 3: A triangular fuzzy number denotes as à = (l,m,u), where l ≤ m ≤ u, has the following triangular type membership function; x < l,  0,  ( x − l ) / (m − l ), l ≤ x < m,  µ A% ( x) =  1, (eq. 2) x = m, (u − x) / (u − m), m < x ≤ u,  u < x.  0, where l and u are the lower and upper bounds of the fuzzy number Ã, respectively, and m is the midpoint (Figure 4).

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µ A% ( x )

A%

1

l

m

u

Figure 4. A triangular fuzzy number Ã. The Chang’s approach for the FAHP method is stated in as follows: Let X= {x1, x2, x3,…, xn} be an object set and U= {u1, u2,…, um} be a goal set. The extent analysis for each goal is performed under each object. Therefore, m extent analysis values for each object are indicated with the following parameters: M g1i , M g2i ,..., M gmi , i=1, 2,…, n, (eq. 3) where all the M gj (j=1,2,…,m) are TFNs. The steps of Chang’s extent analysis can be given as in the following: Step 1: The value of fuzzy synthetic extent with respect to the ith object is defined as

 n m  Si = ∑ M ⊗  ∑∑ M gji  j =1  i =1 j =1  m

−1

j gi

(eq. 4)

m

To obtain ∑ M gji , the fuzzy addition operation of m extent analysis values for a particular j =1

matrix is performed such as: m m m  m  j M = l , m ,  ∑ j ∑ j ∑uj  ∑ gi j =1 j =1  j =1 j =1 

(eq. 5)

−1

 n m  And to obtain ∑∑ M gji  , the fuzzy addition operation of M gji (j=1, 2,…, m) values is  i =1 j =1  performed such as: n m m m  m  j M = l , m , (eq. 6) ∑∑ gi ∑ j ∑ j ∑uj  i =1 j =1 i =1  i =1 i =1  and then the inverse of the vector in Eq. (9) is computed, such as:     1 1 1  j   . M = , , ∑∑ gi  n n n  u   i =1 j =1   ∑ i ∑ mi ∑ li   i =1 i =1 i =1  n

m

−1

(eq. 7)

Step 2: The degree of possibility of M2= (l2, m2, u2) ≥ M1=(l1, m1, u1) is defined as (eq. 8) V ( M 2 ≥ M 1 ) = sup  min( µ M1 ( x ), µ M 2 ( y ))  y≥x

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and can be expressed as follows: V (M2≥ M1) =hgt (M1∩ M2)   1, if m2 ≥ m1 ,  0, if l1 ≥ u2 , = µM 2 (d ) =   l1 − u2  , otherwise.  (m2 − u2 ) − (m1 − l1 )

(eq. 9)

Figure 5 illustrates Eq. 9 where d is the ordinate of the highest intersection point D between µ M1 and µ M 2 . To compare M1 and M2, we need both the values of V (M1≥M2) and V (M2≥ M1).

M2

M1

V (M 2 ≥ M1 )

0 l2

m2 l1 d u2 m1

u1

Figure 5. The intersection between M1 and M2. Step 3: The degree possibility for a convex fuzzy number to be greater than k convex fuzzy Mi (i=1,2,…,k) numbers can be defined by V (M ≥ M1, M2,…, Mk) =V [(M ≥ M1) and (M≥M2) and … and (M ≥ Mk)] =min V (M ≥ Mi), i=1,2,3,…,k. (eq. 10) Assume that d'(Ai) = min V(Si ≥ Sk) for k=1,2,…,n; k≠i.. Then the weight vector is given by W' = (d'(A1), d'(A2),…,d'(An))T

(eq. 11)

where Ai (i=1, 2,…, n) are n elements. Step 4: Via normalisation, the normalised weight vectors are W= (d(A1), d(A2),…,d(An))T,

(eq. 12)

where W is a non-fuzzy number. 2.2.1 The consistency calculation under fuzzy environment The acceptance of pairwise comparison matrix of decision makers is based on the consistency calculation and it defines whether matrix is robust or not. In the existing literature, consistency control is considered for the AHP method whereas it is usually ignored for the FAHP method. Bulut et al. (2012) proposed centric consistency index (CCI) which is based on the geometric 11

consistency index (GCI) (Aguarón & Moreno-Jiménez, 2003; Crawford & Williams, 1985) for the consistency control for the FAHP method and its calculation is as follows; Let A=(aLij,aMij,aUij)n×n be a fuzzy judgement matrix, and let w=[(wL1,wM1,wU1), (wL2,wM2,wU2),…,(wLn,wMn,wUn)]T be the priority vector derived from A using the row geometric mean method. The CCI is calculated by aLij + aMij + aUij w + wMi + wUi 2 (log( ) − log( Li ) ∑ (n − 1)(n − 2) i < j 3 3

CCI ( A) =

+ log(

wLj + wMj + wUj 3

(eq. 13)

))2

where n is the number of elements. The CCI method is used to calculate each decision maker’s pairwise matrix and the thresholds of GCI (Aguarón et al., 2003) is applied for the CCI and its scale is GCI =0.31 for n=3; GCI =0.35 for n=4 and GCI =0.37 for n>4. 2.2.2 The prioritisation of decision maker The weight of each decision makers could not be same because of difference of their experience and knowledge. The eigenvector (EV) and row geometric mean method (RGMM) are used to derive priorities for individual decision makers that can be used for aggregating group preferences of individuals in the literature (Cao, Leung, & Law, 2008; Forman & Peniwati, 1998; Ramanathan & Ganesh, 1994). The levels of expertise and survey consistency are assumed to be correlated and the normalised inverse of CCI value for the each decision maker’s matrix is proposed for the weight of corresponding decision maker in this paper. The algorithm of proposed method is as follows: Let D = {d1, d2,…, dm}be the set of decision makers, and λk = {λ1, λ2,…, λm} be the weight of decision makers. The weight of decision makers (λk) is the normalised Ik for the group of experts which is calculated as follows: 1 (eq. 14) Ik = CCI k where Ik is the inverse of the CCI,

λk =

Ik

(eq.15)

∑ k =1 I k m

where λk>0, k = 1,2,…,m, and ∑ k =1 λk = 1 . m

Let A (k) = (aij( k ) ) n×n be the judgement matrix provided by the decision maker dk. wi( k ) is the priority vector of criteria for each decision maker calculated by

wi( k ) =

(

∏ j =1 aij n

∑ (∏ n

n

i =1

j =1

)

1/ n

)

1/ n

aij

(eq. 16)

The aggregation of individual priorities is defined by

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( w )λ ∏ = ∑ ∏ ( w )λ m

( w) i

w

(k ) i

k =1

n

m

i =1

k =1

k

(k ) i

(eq. 17)

k

where wi( w ) is the aggregated weight vector.

3. SHIPPING PERFORMANCE INDEX AND THE ROLE OF KPI The Shipping performance index is formed by a number of KPIs and the contribution priorities of KPIs are kept identical in the classical approach. This paper investigates the KPIs and their contribution to the SPIs by assessment of the ship-owners’ satisfaction. In the first step, we look for the requirements of ship-owners and these requirements are already presented in the previous section as below: • • • • •

Cheaper Service (CS) Competence with international legal framework (IL) Business network & Cost efficiency(BN) Ensuring the good condition of hull (structural stability & competence) (GH) Quality Manning (QM)

In the second step, we perform a survey and ask to a number of business practitioners to define priority degrees (relative weights) of each requirement. For this purpose, the Fuzzy-AHP framework is used to perform pairwise comparisons and to define final priorities (See Table 3). Table 4 presents the results of the fuzzy-AHP process and cheaper service and business network & cost efficiency are found the most significant requirements. Good condition of hull, Competence to international regulations and Quality manning are following requirements respectively. Table 3. Sample for the pairwise comparisons. Cheaper Service

vs.

Quality Manning

Degree of Importance Equally

Slightly

Moderately

More

Strongly

Extremely



















Table 4. The aggregated fuzzy judgement matrix for the ship-owners’ requirements. CS IL BN CS (1,1,1) (1.19,3.26,5.28) (1,1,1) IL (0.19,0.31,0.84) (1,1,1) (0.24,0.49,1) BN (1,1,1) (1,2.03,4.17) (1,1,1) GH (0.33,1,1) (1,1,2.03) (0.33,1,1) QM (0.20,0.33,1) (0.31,0.85,1) (0.20,0.33,1) CCI = 0.02* *The constraint for consistency is 0.37 for five criteria.

GH (1,1,3) (0.49,1,1) (1,1,3) (1,1,1) (0.42,1,1)

QM (1,3,5) (1,1.18,3.24) (1,3,5) (1,1,2.39) (1,1,1)

Weight (0.27) (0.16) (0.26) (0.18) (0.12)

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The HoQ matrix (Table 5) is established by embedding both fuzzy-AHP results and the relationship degrees which are based on an additional technical survey. Technical survey is performed with professionals in ship management and scholar in shipping business research. Rather than classical surveys, the relationship degrees should be defined by a number of professionals in practice and scholars in related research. For the present research, six researchers defined the preliminary matrix of relationship degrees and these indications are sent to a few practitioners in ship management business to investigate their rationale and validity. After the calibration process, the final matrix is used for further processes. After the calculation of sum products, global and local priority degrees (relative weights) are defined by percentage contributions. The empirical results indicate that the contributions of KPIs particularly differ than the simple average. For example, in case of Health & Safety SPI, the Flawless Port State Control Performance has an outstanding majority among the KPIs (0.52). On the other hand, the Navigational Safety SPI does not extremely differ from the simple average estimation. The Security performance and Technical performance also indicate imbalanced contributions. The Flawless Port State Control Performance (0.67) and Failure of critical equipment and systems (0.81) are the major KPIs for security performance and technical performance respectively. In cumulative results, two KPIs are found very important to improve ship-owners’ expectations. First, the performance at port state controls (PSC) and second is the financial performance (i.e. budget performance). Ship-owners would like to ensure business sustainability in terms of technical and financial aspects. “Failure of critical equipment and systems” is another critical measure for ship management performance and it improves “Vessel availability” together with the PSC performance. A ship-owner eventually requires ensuring financial sustainability and profitability at first while maintaining vessel availability for the present and future charter contracts as a posterior objective. The evidences of the intended study indicated how the priorities and ship-owners’ perception may differ for a specific SPI. Based on the empirical results, it is recommended that the contribution degree of KPIs should be recalculated for a specific ship-owner according to his particular needs and perception of KPIs. In the classical approach, SPIs may indicate the average quality of ship management service, but it is limited to general perspective rather than the uniqueness of the ship management company. Another drawback of the classical approach is the insignificance of averaging method. Although, this paper illustrates the results for a pilot group, an extended survey may ensure the more robust and widely accepted priority degrees. The proposed method enables the ship-owner can easily assign his own priority degrees and calculate the original SPI indices for a number of ship management companies by using their KPI results. The provided flexibility may also contribute to the mutual trust and the attractiveness of the 3P ship management market. 5. CONCLUSION This paper proposed to establish an alternate process for aggregating the KPIs to SPIs by utilising the HoQ matrix design of the QFD approach. The superiority of HoQ is based on the transformation of the requirements into technical and practical outcome. In case of SPI framework, KPIs represent the technical measures for ship management performance. A gap exists between KPIs and their corresponding priority degrees based on the level of contribution to the ship-owners’ requirements. The HoQ matrix is employed to elicit the relationship between the requirements and the KPIs. Rather than the existing form of SPIs, the priority of KPIs found variety according to their contribution to ship-owners’ satisfaction. Therefore, it is recommended to perform an initial customer survey to expose intrinsic expectations of ship-owners from the 3P ship management service. By defining the relative weight of requirements and their relationship 14

degrees with KPIs, the customer-oriented value of SPIs will be established which is more practical and useful from the ship-owners’ perspective. An existing gap on this approach is the cross-investigation of technical measures (KPIs). Some of KPIs have very close relationship and the reliability of this structure should be discussed based on whether the independence of TMs is required to improve practical implications. For example, Flawless Port State Control Performance (FPSC) and Health and Safety deficiencies (HSD) are highly correlated. However, 3PSM companies are not able to elicit whether the importance of FPSC is unique and independent than HSD or it is almost derived from HSD. Since HSD is a part of FPSC, such paradoxes may arise. A future research may clarify such drawbacks and InterManager may consider a revision of existing KPI framework based on these evidences. ACKNOWLEDGEMENT Authors are indebted to many business practitioners and scholars from the shipping industry for their valuable comments and contribution to the present study.

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Table 5. House of quality matrix for ship-owners’ satisfaction

Max. value in column

9

5

5

1

5

9

9

9

1

5

9

9

9

9

9

5

5

9

9

5

5

5

Sum product

4.87

1.05

1.71

0.56

1.22

2.20

3.30

3.30

0.40

1.71

1.93

2.20

2.79

1.60

1.60

3.72

2.80

2.86

6.04

2.82

1.05

1.71

Relative weight (Global)

0.07

0.02

0.02

0.01

0.02

0.03

0.05

0.05

0.01

0.02

0.03

0.03

0.04

0.02

0.02

0.05

0.04

0.04

0.09

0.04

0.02

0.02

1.22

● ▲ ▲

● ■

9

9

9

2.55

5.12

3.04

0.04

0.07

0.04

4.87

Failure of critical equipment and systems

Condition of class

● ■

Tech

Security deficiencies

▲ ▲ ■

Flawless Port State Control performance (See Health & Safety SPI)

■

Security

Vetting deficiencies

▲ ▲ ▲ ■

● ■

Vessel availability

●

Port state control detention

●

■

▲ ● ● ● ● ▲ ● ▲ ● ▲ ▲

Passenger injury ratio (See Health & Safety SPI)

▲ ●

■

Operational deficiencies

■

■

Cargo related incidents

■

●

■ ● ■

Drydocking planning performance

▲

●

Operational Performance

Budget performance

●

▲ ■ ▲ ▲

Navigational incidents

Environmental deficiencies

▲ ▲ ● ▲ ▲ ■

●

Navigational deficiencies

Contained spills

QM

Ballast water management violations

GH

0.12

Releases of substances as def by MARPOL Annex 1-6

0.18

9

Training days per officer

9

■

● ● ▲ ▲ ▲ ▲ ▲ ▲

Officers experience rate

BN

■

● ●

Officer retention rate

0.26

Nav

● ■

● ▲

Cadets per vessel

9

Environmental

● ● ▲ ▲ ▲

▲* ● ■ ●

HR deficiencies

IL

Crew planning

0.16

Crew disciplinary frequency

9

Passenger Injury Ratio

CS

Lost Time Sickness Frequency

0.27

Health and Safety deficiencies

Relative weight

9

Human Resources

Lost Time Injury Frequency

Max. value in row

Flawless Port state control performance

Health and Safety

● ■

▲ ● ▲

▲

▲ ■ ▲

9

5

9

2.37

1.08

4.44

0.03

0.02

0.06

Relative weight (Local) 0.52 0.11 0.18 0.06 0.13 0.15 0.22 0.22 0.03 0.11 0.13 0.15 0.29 0.17 0.17 0.38 0.49 0.51 0.26 0.12 0.04 0.07 0.05 0.11 0.22 0.13 0.67 0.33 0.19 0.81 *

“■”, strong relationship (9), “▲” moderate relationship (5), “●” low relationship and “Ø“ no relationship.

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