National freight demand modelling: a tool for macrologistics management

• DOI: https://doi.org/10.1108/IJLM-11-2017-0290
• Pages: 1171-1195
• Date: 07 September 2018
• Published date: 07 September 2018
• Author: Jan Hendrik Havenga,Zane Paul Simpson
• Subject Matter: Logistics,Management science & operations

National freight demand

modelling: a tool for

macrologistics management

Jan Hendrik Havenga

Department of Logistics, Stellenbosch University, Stellenbosch,

South Africa, and

Zane Paul Simpson

Stellenbosch University, Stellenbosch, South Africa

Abstract

Purpose –The purpose of this paper is to present the results of South Africa’s national freight demand

model and related logistics cost models, and to illustrate the application of the modelling outputs to inform

macrologistics policy.

Design/methodology/approach –Spatially and sectorally disaggregated supply and demand data are

developed using the input-output (I-O) model of the economy as a platform, augmented by actual data. Supply

and demand interaction is translated into freight flows via a gravity model. The logistics costs model is a

bottom-up aggregation of logistics-related costs for these freight flows.

Findings –South Africa’s logistics costs are higher than in developed countries. Road freight volumes

constitute 80 per cent of long-distance corridor freight, while road transport contributes more than 80 per cent

to the country’s transport costs. These challenges raise concerns regarding the competitiveness of

international trade, as well as the impact of transport externalities. The case studies highlight that domestic

logistics costs are the biggest cost contributor to international trade logistics costs and can be reduced

through inter alia modal shift. Modal shift can be induced through the internalisation of freight externality

costs. Results show that externality cost internalisation can eradicate the societal cost of freight transport in

South Africa without increasing macroeconomic freight costs.

Research limitations/implications –Systematic spatially disaggregated commodity-level data are

limited. There is however a wealth of supply, demand and freight flow information collected by the public and

private sector. Initiatives to create an appreciation of the intrinsic value of such information and to leverage

data sources will improve freight demand modelling in emerging economies.

Originality/value –A spatially and sectorally disaggregated national freight demand model, and related

logistics costs models, utilising actual and modelled data, balanced via the national I-O model, provides

opportunities for increased accuracy of outputs and diverse application possibilities.

Keywords Macrologistics, Modal shift, Externalities, Evidence-based policy, South Africa

Paper type Research paper

Introduction

Unabated population growth, urbanisation, and resulting increased consumption is

expected to intensify pressure on transport services (Ivanova, 2014). This is exacerbated by

the global reality that transport infrastructure is approaching capacity levels (Müller et al.,

2012) and the need to enable full cost trade-offs (i.e. including externalities) in purchasing

and logistics decision making to reduce logistics’environmental impact (McKinnon, 2014;

Liljestrand et al., 2015).

Disaggregated national freight demand modelling will be a key enabler for the

management of transport and logistics as a macroeconomic production factor within this

changing landscape. Logistics- and connectivity-related interventions are estimated to have

the highest potential to reduce trade costs and to boost global value chain integration

(World Bank, 2016). Zaman and Shamsuddin (2017) estimated that timeliness of logistics

has a significant impact on per capita income. Coto-Millán et al. (2016), using Logistics

Performance Index (LPI) data, estimated that every 1 per cent increase in LPI, ceteris

paribus, increases domestic technical efficiency by 0.59 per cent.

The International Journal of

Logistics Management

Vol. 29 No. 4, 2018

pp. 1171-1195

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-11-2017-0290

Received 4 November 2017

Revised 9 March 2018

28 May 2018

6 July 2018

Accepted 15 July 2018

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

1171

National

freight demand

modelling

Almost four decades ago, Kojima (1982) reflected on the importance of incorporating the

impact of transport costs and input-output (I-O) interactions into macroeconomic theory.

Lakshmanan and Anderson (2002) emphasised the need for performance-based research to

demonstrate the link between logistics infrastructure investment and economic growth.

Liu et al. (2006) referred to the dynamic correlation between macroeconomic developmentand

logisticsdevelopment that supportsand enables economic growth.Yet, Tavasszy and De Jong

(2014) still lamented the sluggish development of measurement tools to inform and evaluate

national freight transport policies, andascribe this mainly to the failure of viewing transport

and logistics as a component of public policy and national competitiveness,in line with other

macroeconomicmeasurements. In South Africa, outof the 86 development indicators tracked

by the South Africangovernment (Departmentof Planning, Monitoring and Evaluation, 2014),

not one refers to freight logistics, or the key role it plays in the country’s development and

economic competitiveness. The same holds true for the regular macroeconomic indicators

tracked by the South African Reserve Bank and Statistics South Africa.

The objectives of this research are to present the results of South Africa’s national freight

demand model (FDM) and related cost models, and to illustrate the application of the

modelling outputs to inform the macrologistics policy debate.

In the context of this research, national freight demand modelling refers to the

development of a model that estimates the total volume of freight movements within a

country. This therefore includes all domestic flows as well as international trade (i.e. cross-

border tradeand maritime imports and exports).Spatial and sectoral disaggregationof such a

national FDMis essentialfor the model to have meaningful applicationsto address challenges

within the nation’s freight flow landscape. The logistics costs model takes the volumetric

outputs a step further by quantifying the logistics and externality costsassociated with each

flow to inform policies and investments for improving the country’s competitiveness.

The paper is structured is as follows. The literature survey describes the rationale of

national freight demand modelling within the context of the public policy lifecycle, followed

by an overview of the history of this type of modelling to inform the methodology which is

subsequently presented. The results section first presents the aggregate outputs of South

Africa’s FDM, followed by the application of the modelling outputs to inform identified

macrologistics challenges. The concluding remarks summarise the research outputs and

resulting proposed macrologistics policy interventions.

National freight demand modelling: rationale and historical perspectives

The rationale for national freight demand modelling

Four decades ago, Van Es (1977) eloquently described the purpose of national freight

demand modelling, which still holds true today (De Jong et al., 2016), namely:

•conducting impact assessments of transport policy alternatives;

•informing policy measures to influence optimal modal competition;

•informing measures to improve transport infrastructure and related policy;

•estimating the future size and composition required per mode based on the volume

and composition of future transport demand and socio-economic initiatives to

influence spatial location and demand patterns;

•conducting cost-benefit analysis for decisions on transport infrastructure projects for

rail, road, intermodal transport, ports and hinterland connections; and

•in the long run, inform regional economic policies with regard to spatial integration

(land-use planning), i.e. the extent to which the spatial integration of economic

activities can be altered in the light of infrastructure policy or vice versa.

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