Potential Methods for the Research Problem

This research attempts to assess and analyse the ex-post security impacts on the operational efficiency and performance benchmarking of container terminals. The research problem can be formulated as follows: ‘what is the impact of procedural security on the efficiency of container port and terminal operations?’

To direct the problem more precisely, three research questions are used:

• Q1: What is the operational and procedural scope of port security programmes?

• Q2: How can container-port operational efficiency be measured and benchmarked?

• Q3: How can we measure and quantify the impact of procedural security?

Answering these questions offers grounds for selecting applicable research tools and techniques of analysis. Based on the above discussion about the need to understand port practice, security procedures must be captured in terms that fit container-port configurations, operating sites, and handling systems. This could be then linked to the measurement of operational efficiency, providing comparative benchmarks of productivity changes before and after the introduction of port security measures.

Security impacts can therefore be assessed in terms of efficiency gains or losses, both over time and across container terminals. To conform to this approach, three analytical techniques are required, namely:

(1) Prescriptive modelling for mapping terminal processes and security procedures, (2) Analytical benchmarking for measuring and comparing container-port efficiency, (3) Productivity change analysis for assessing the impacts of security regulations.

IV.2.1 Process Description and Function Modelling: IDEF0

Process modelling uses a variety of tools such as systems engineering, functional economic analysis, Petri-nets, and IDEF (Integration Definition) techniques. The IDEF methodology was derived from a well-established graphical language known as the structured analysis and design technique (SADT). In the late 1980s, the US Air Force launched the Integrated Computer Aided Manufacturing (ICAM) project to develop a modelling method to help with designing and managing its process of supplier development and evaluation. The IDEF family includes several tools each for modelling a particular perspective, with IDEF0 for function modelling being the most suitable for prescriptive mapping of terminal operating processes and security procedures. Function and process modelling provide the framework required to analyse and redesign workflows and business processes of actual container-port operations and achieve improvements in system’s performance both at individual and aggregate operating processes.

IDEF0 models are composed of three types of information: graphic diagrams, text, and glossary. The graphic diagram is the major component of an IDEF0 model, containing boxes, arrows, box/arrow interconnections, and associated relationships. In its original form, IDEF0 includes both a definition of a graphical modelling language (syntax and semantics) and a description of a comprehensive methodology for developing models.

The two primary modelling components are functions represented on a diagram by boxes, and the data and objects linking those functions and represented by Inputs, Controls, Outputs, and Mechanisms (ICOM) arrows. The semantics of IDEF0 boxes and arrows is shown in Figure 13 below.

Figure 13: Semantics of IDEF0 box and arrows (Source: Author)

The result of applying IDEF0 to a system is a model that consists of hierarchical cross-referenced series of diagrams, text and glossary. Boxes or functions are decomposed into diagrams that are more detailed until the subject is described at a level necessary to support the goals of a particular project. As illustrated in Figure 14, the top-level diagram of the model provides the most general or abstract description of the subject. It is then followed by a series of child diagrams providing more details about the subject.

For a detailed description of the IDEF0 method, the reader is referred to Mayer (1992), Colquhoun et al. (1993), and Jorgensen (1995).

Figure 14: IDEF0 decomposition structure (Source: Barletta and Bichou, 2007)

Function or activity

Controls

(Factors that constraint the activity)

Mechanism

(Means used to perform the activity)

Inputs Outputs

Over the years, a series of standard IDEF0 functional modelling diagrams were developed for different system enterprises such as manufacturing, production, and logistics systems (Slats et al., 1995). There is indeed an extensive literature on various applications of the IDEF0 technique in the logistics industry, but with only a few applications in ports - see for instance Paik and Bagchi (2000), and Barletta and Bichou (2007).

IV.2.2 Analytical Benchmarking: DEA Models and Site-Specific Datasets

The objective of benchmarking is to compare the efficiency of carrying out a particular activity or group of activities either at a point in time or over time. In Chapter III, we reviewed benchmarking methods applicable to port operations and demonstrated that any benchmarking analysis should be defined relative to an assessment of best practice, in other words the level of efficiency should be measured relative to an efficiency frontier. We also showed that several benchmarking techniques can be used to estimate the efficiency frontier and these are classified into two main categories: econometric (parametric) techniques versus programming (non-parametric) techniques. Econometric models require an assumption about the relationship between inputs and outputs and estimate the parameters of a cost or a production function. Programming models, in contrast, relate outputs to inputs without recourse to econometric estimation and the efficiency is estimated directly from the data.

Further discussions on the advantages and disadvantages of each technique as well as on the features of port operating systems have shown that programming techniques are most suited to benchmarking operational efficiency for assessing the ex-post impacts of procedural security. In particular, the structure of container port production depicts different handling configurations and operating systems, which makes the estimation of a functional form under SFA very difficult to apply in the context of international port benchmarking. Programming techniques are less restricted to sample size than econometric models, and can estimate technical efficiency for both individual inputs and the overall production process. Moreover, both the multi-output nature of port production and the lack of detailed data are likely to limit the practicality and reliability of econometric methods. On such grounds, we advocate the use of programming techniques namely in the form of a series of data envelopment analysis (DEA) models.

In order to estimate and compare efficiency scores under a stationary frontier over time, we conduct contemporaneous and inter-temporal DEA analyses using cross-sectional and panel data, respectively. In the context of cross-sectional data, the contemporaneous approach compares observation units within the same time-period, e.g. a year. In the context of panel data, the inter-temporal approach pools all data over the total time observed, e.g. total number of years. By using both approaches, a DMU is benchmarked against varying sample sizes while still assuming constant technology over time.

In addition to estimating the efficiency of DMUs within the aggregate dataset, contemporaneous and inter-temporal approaches are also used to analyse the efficiency of observation units relative to alternative DEA models and site-specific datasets. The utilisation of different DEA models and datasets conforms to the objectives of this research in terms of analysing the interplay between terminal sites and operating configurations. On the one hand, container terminal systems portray different operating configurations that require alternative DEA models capable of capturing the variations in handling and production technologies between and within terminals. On the other hand, the structure of container terminal production depicts a network-type operating process that necessitates detailed analysis by site-specific and network-related efficiency. The specification and operationalisation of relevant DEA models and site-specific datasets are provided in Chapter V.

IV.2.3 Productivity Change Analysis: TFP Malmquist DEA

Although contemporaneous and inter-temporal analyses are useful for estimating and comparing technical efficiency, they can be misleading in a dynamic context because neither approach accounts for possible shifts of the frontier over time. Furthermore, there is no means of checking whether the frontier is moving or stationary over time.

To ensure a DMU’s efficiency is tracked over time while allowing for shifts in the efficiency frontier, several time-dependent versions of DEA have been developed, notably DEA window analysis. Under DEA window analysis, also referred to as window DEA, DMUs in selected time-periods are included simultaneously in the benchmarking analysis. Depending on the width of the window, the technique may be conducted in terms of contemporaneous, inter-temporal and locally inter-temporal analyses (Charnes, 1985; Asmild et al., 2004). Contemporaneous and inter-temporal analyses correspond to the basic DEA approaches described above where the window width is equal to 1 (one) and T (total time or number of years observed), respectively.

The locally inter-temporal analysis compares subset DMU observations at different but successive time windows where each DMU-observation is only compared with the alternative subset in the single window, assuming a constant frontier during each window. Under this approach, the window width is larger than one and less than all periods combined, but it is usually set for a three-year period. Cullinane et al. (2004) used this approach when they applied DEA windows analysis to track the productive efficiency of 25 major container ports between 1992 and 1999.

Although the locally inter-temporal window analysis seems an attractive technique for tracking changes in efficiency over time, it has many limitations. First, the technique is akin to a moving average procedure where the technology remains constant in each window. Second, a DMU under window DEA is only compared with a subset of data and not with the whole data set. Indeed, the width of the window is usually defined arbitrarily given that no underlying theory or analytical evidence that validates the choice of a particular window size exists. In the context of benchmarking container-port

efficiency, the overlapping subsets derived from successive windows wrongly imply that the container port production is somehow discontinuous over the study period. Last, but not least, because the efficiency of a DMU observation in a particular window is calculated more than once and hence included in several windows, it is not obvious how to define the frontier in the same window-period. This issue hinders the application of total factor productivity (TFP) analysis such as through the Malmquist productivity index (MPI). For instance, Asmild et al. (2004) recommended that it is not appropriate to decompose Malmquist indices based on window DEA into standard frontier shift and catching up effects.

In view of the above, we advocate the use of Malmquist DEA in favour of window DEA. The Malmquist TFP index, or Malmquist Productivity Index (MPI), requires the estimation of a distance function but the latter can be directly specified under DEA. The approach adopted in this thesis is to apply a stepwise Malmquist DEA analysis, both on a year-by-year basis and on a regulatory-period basis.

In applying the stepwise Malmquist DEA, we can exploit panel data for both efficiency measurement and analysis of TFP growth. This approach provides a sound basis for benchmarking container-terminal efficiency with a view to tracking the shifts in frontier technology over time. The calculation of the MPI should also indicate whether any convergence in port productivity rates has taken place over time, especially in the aftermath of the new security regulations. Another advantage of the MPI is the ability to decompose total factor productivity into various sources of efficiency, mainly into a measure of total technical efficiency change (TEC) representing the catching up effect and a measure of technological change (TC), which represents the shift in frontier technology. TEC can be further decomposed into a measure of pure technical efficiency change (PEC) and a measure of scale efficiency change (SEC). This can shed further light on the interplay between the impacts of procedural security and the sources of changes in TFP over time and between container terminals.