Macroeconomics as a discipline informs the overall strategic management of the country’s economy in terms of the contribution of holistic components, as well as the interaction between these components. This is achieved through measuring, modelling, reporting and strategising around these components, as well as the whole, normally aggregated as benchmarks such as gross national product (GNP) and GDP. In short ‘[it] is the study of the behaviour of the economy as a whole’ (Samuelson & Nordhaus 1989:76). Mohr and Fourie (1996) continued by setting specific goals for the macro-economy, that is, growth, full employment, price stability, external (balance of payments) stability and fair income distribution. In order to achieve these objectives, the components or ‘production factors’ of the economy need to achieve optimal interaction and output, very much in the same way as in which the production factors of a firm (on the micro level) is expected to achieve optimal interaction and output. Terreblanche (1994) described economic systems as the various methods that countries (or communities) can use to organise their economic life in the broadest sense. He contended that an economic system requires economic activities that are collated according to a certain principle or principles into a coherent structure and that this structuring activity should be enforceable.

The process of economic organisation within a community is therefore total (it includes all aspects of economic life), structured (collated according to principles) and enforceable (not only spontaneous, but by a specified power). The resulting macro-organisation’s existence is qualified by certain objectives (e.g. increased output, high employment and stable prices), and this qualification is achieved by certain instruments that involve trade-offs or choices (Samuelson & Nordhaus 1989).

This process is also evident in the well-developed field of performance management on an organisational level (i.e. at a microeconomic level). Kaplan and Norton (1996) elevated this discipline with the development of the balanced scorecard, where the financial perspective is supplemented by an integrated set of measures that links customers, internal business processes and employee development to managerial and business performance. Keebler et al. (1999:34) referred to dramatic improvements that are enabled by effective performance measurement on a complete and integrated business process level, warning against a ‘narrow functional focus’ that can have counterproductive effects on the performance of the supply chain as a whole.

Kaplan and Norton (1996) also related the balanced scorecard back to strategy and the fundamental question of identifying the core business and the rationale for its existence, an iconic question first posed by Theodore Levitt nearly 50 years ago (Levitt 1960). Cobbold and Lawrie (2002:2) maintained that ‘for control to be effective, it needs to be informed about both the activities and results delivered by the organisation, and the organisation’s prior expectations concerning both’. Morgan (2004) also urged managers to understand the big picture while guarding against generic solutions to business challenges. To relate performance measurement to objectives seems axiomatic but, in the context of logistics as a macroeconomic production factor, it has not yet become an accepted practice.

Demkes and Tavasszy (2000:15–16) discussed the difference between macro- and micro-level indicators in-depth and found the problem in the meso area (refer Figure 1), because:

The question arises about the level of indicators that measures the objectives for an important macroeconomic system, such as a corridor. In the South African economy, ‘cluster studies’ were initiated by the Department of Trade and Industry to evaluate and consider the contribution, effectiveness and prospects of various economic clusters in South Africa (Hirsch 2005). Implementation of strategies resulting from these studies was challenging; the macroeconomic approaches were not necessarily geared towards microeconomic principles in economic management, pointing to a gap in what Demkes and Tavasszy (2000) labelled ‘meso-level’ indicators. The studies did, however, highlight, amongst others, inefficiencies within industries, as well as in relationships between industries (Hirsch 2005:154). These inter-industry relationships include the distribution of resources in the economy (i.e. logistics). A corridor, to a large extent, mimics an industrial cluster. Two decades after the realisation of the importance of a coordinated sectoral view for industrial development, there is also a recognition that a similar coordinated view is required to manage logistics as a national production factor. Given the nature of the macro freight logistics landscape discussed in the introduction, priority was given to a CPMS.

The complexity of the LSP industry and the plethora of freight owners involved on corridors render individual stakeholder consultations impossible. The most effective approach is to obtain buy-in and inputs through industry association engagements. Industry associations are mature, active substrata of the South African economy, playing significant intra- and inter-industry, and governmental liaison roles. The industry associations listed in Box 1 were identified as key stakeholders and participated in one or more workshops. System development was an iterative process of desktop research, workshop input and stakeholder alignment. The final sign-off was, however, the prerogative of the sponsor.

The CPMS development was facilitated by the availability of established research to quantify logistics costs and transport volumes in South Africa, refined over the past two decades. The Freight Demand Model (FDM) is a demand-side model, based on the national input–output model, estimating supply and demand of commodities in geographical areas, and translating this into modal share through gravity modelling, currently for 83 commodity groupings in 372 geographical areas with a 30-year forecast at 5-year intervals for three scenarios (Havenga 2013). Results are calibrated with industry research and correlated with known freight flows (i.e. rail, pipelines and conveyor belts). The NFFM is a supply-side model that translates vehicle counts into pre-defined long-distance and rural road freight flows (Havenga & Pienaar 2012b). By combining the functionalities of the FDM with the NFFM, monthly bi-directional commodity flows can be estimated for road and compared to actual rail shipments in order to determine modal market share. The model will also, based on the detailed vehicle class information available from traffic counts, be able to determine target rail market share in real time, that is to identify road freight that is rail-friendly.

The logistics cost model is a bottom-up aggregation of logistics-related costs for commodity-level tonnes produced and imported, comprising transport, storage and port-handling costs, management and administration costs, and inventory carrying costs (Havenga 2010). The model was recently expanded to include detailed externality cost modelling per mode pertaining to accidents, congestion, emissions, noise, policing and land-use value (Havenga 2015). De Jong et al. (2013) recognised that the inclusion of logistics modules in national and international freight transport models within Europe since 2004 has been critical to inform and evaluate policy measures.

It is important to note that although the availability of these models provide a significant leapfrog-advantage for the population of a CPMS (compared to most other countries), these models were developed to, at first, quantify South Africa’s macro freight logistics landscape. As with any macroeconomic model, challenges are experienced in disaggregation, and population of the CPMS cannot purely rely on disaggregated values from the national models. Rigorous interrogation of the data with knowledgeable stakeholders, and updates as required, is imperative, both to enhance the usefulness of the outputs and to engineer continuous stakeholder buy-in through data validity.

South Africa has two distinct freight corridor systems, the nature of which dictates the development of two discrete CPMS dashboards. These are the bulk export corridors that include the two world-class ring-fenced export corridors (Richards Bay coal exports and Sishen-Saldanha iron ore exports) and manganese exports from the Northern Cape to Port Elizabeth, and the second system is the multi-modal, general freight corridors (refer Table 1).

The benefit of the availability of the established FDM described previously is that South Africa’s multi-modal freight corridors have been defined from first principles – how traffic actually behaves in the economy. This fast-tracks the development and implementation of a CPMS as corridor definition is an onerous task at the outset of a CPMS (Dion & Skabardonis 2015). South Africa’s most dense multi-modal corridors are Gauteng-Durban and Gauteng-Cape Town (refer Figure 2).

The CPMS is developed as a permanent mechanism managed under the auspices of the Department of Public Enterprises, supported by the Ministries of Transport and Trade and Industry, to monitor and address corridor performance in an intelligent, systematic and constructive manner. The following vision and objectives will steer the activities of the CPMS:

The guiding principles for decision-making are as follows:

A Steering Committee (Steercom), with representatives from major industry associations (comprising LSPs and freight owners), state-owned infrastructure owners and operators (Transnet and SANRAL), the sponsor Department of Public Enterprises, and supporting Ministries of Trade and Industry, and Transport, should be established.

The purpose of the overarching metrics of the CPMS is to track aggregate corridor performance. The three metrics that have been selected in the foundation framework are as follows:

These metrics can be aggregated to reflect the two macroeconomic logistics indicators proposed by Havenga (2010), namely:

To support the data needs of the different stakeholders and facilitate analytics in support of the overarching metrics, three measurement perspectives have been identified for each of South Africa’s freight corridors targeted for the CPMS, namely, a policy, customer service and infrastructure perspective (informed by Farzaneh et al. 2012; Martner & Garcia 2015; Punungwe et al. 2009). The KPAs, metrics, rationale for inclusion and the measure of success for each metric (to be calculated per corridor) are described below for each measurement perspective for both multi-modal and bulk corridors.

The four bi-directional multi-modal corridors currently included in the CPMS are as follows:

These corridors contribute approximately two-thirds of the countries general freight tonne-km.

South Africa only has three dedicated bulk corridors, which are all included in the first phase of the CPMS, namely:

Given the nature of the competitive dynamics on the bulk export and multi-modal corridors, the policy perspective contains different measurement elements, as described in Tables 2 and 3. Table 4 and Table 5 contain the customer service perspective and the infrastructure perspective for both sets of corridors, respectively (the few unique attributes pertaining to bulk corridors are highlighted). (This is for ease of publication. From a systems point of view, each corridor will have a separate dashboard to optimise user interface).

The initial objective is to measure the core traffic descriptors and their variance (price, time, volumes and infrastructure, refer to literature survey), as advocated by Harrison et al. (2006); Hartmann (2013); Punungwe et al. (2009) and Martner and Garcia (2015).

From a policy perspective, for the multi-modal corridors, this translates into measures to reduce the cost of freight logistics which, in South Africa, in the short to medium term, centres on modal shift to rail because of the complete absence of intermodal solutions (refer Table 2). From a policy perspective, for the bulk export corridors, efficient throughput is paramount, underpinned by equitable access as well as the alignment of producer demand, rail capacity provision and port capacity provision (refer Table 3).

From a customer service perspective, customers are concerned with service reliability and efficiency as contracted, and at costs and cycle times that facilitate their own competitiveness (refer Table 4). This holds true for both bulk and multi-modal corridors.

Underpinning both the policy and customer perspective is the infrastructure perspective, with the aim to ensure that sufficient capacity is provided and that this capacity is both available when required and utilised optimally (refer Table 5).

The purpose of this article is to establish the rationale for developing a CPMS and to describe the foundation framework of South Africa’s CPMS. The initial population of the system is not yet completed, and results will be premature. Some overarching observations can, however, be gleaned from the initial population of the system for illustrative purposes. These examples are limited to the policy view.

The following key situational strengths aided the development of South Africa’s CPMS:

The following challenges emerged and require management:

In the short term:

In the medium term:

In the longer term, the sourcing of truck GPS data to enhance road origin-destination modelling is a major addition to the data sources that is being investigated.

On a broader level, the role of corridors in facilitating regional economic development, inclusive growth and market access improvements, should be analysed, and the incorporation of related strategies and indicators in the CPMS investigated.