Logistics Productivity Indicator – Assessing Supply Chain Performance

by Dr Koh Niak Wu, Founder, Cosmiqo

Developed on similar lines as the World Bank’s Logistics Performance Index, the Logistics Productivity Indicator (LPI) serves as a quantitative benchmarking tool to help business track and raise their performance in transport, trade and logistics.

An industry initiative by Supply Chain Asia, LPI was a thought experiment designed as a first step to understand the relative performance of warehouses in a quantitative and objective manner. It aims to answer the following questions:

• How does one firm compare to another?
• Do larger warehouses perform more efficiently?
• Do capital-intensive warehouses perform more efficiently?
• What remedial actions can business adopt to improve?

Performance assessments in warehousing are needed to identify the options in design and operations that confer the greatest benefits. There are two important but distinct approaches to performance measurement: economic (i.e. revenue related to cost) and technical (i.e. outputs related to inputs).

Economic performance assessment is somewhat difficult because warehouses typically do not generate revenue; rather, their function is to support the supply chain. For this reason, technical measures based on the output generated and resources consumed tend to yield a clearer picture of operational performance when assessing warehouses across a pool of warehouses.

Basic efficiency concepts

Efficiency can be simply defined as the ratio of observed output to some given inputs. More output per unit of input factor reflects relatively greater efficiency. If the greatest possible output per unit of input is achieved, then a state of absolute or optimum efficiency has been achieved and it is not possible to become more efficient without new technology or other structural or scale changes in the production process. Loosely, a firm is productive if it can produce more with less (but this now raises the question of how ‘productive’ is defined).

An independent analysis of efficiency becomes rather challenging in complex situations, such as when,

i. there are multiple outputs and inputs which cannot be readily analysed with other techniques such as ratios,
ii. the number of organisations being evaluated is so numerous that management cannot afford to evaluate each organisation in-depth, and
iii. the relative performance across a set of firms is required. This then warrants the exploration of other analytical techniques that can sufficiently address these shortcomings.

Using Data Envelopment Analysis (DEA)

DEA is a robust linear programming method used to measure the relative efficiency of multiple decision-making units (DMUs) when the production process presents a structure of multiple inputs and outputs. It has been extensively studied for over 30 years and is commonly used to evaluate the relative efficiency of a number of firms by evaluating its performance against the set of firms considered in the analysis. This also implies that the relative performance of a firm will change as the set of firms increases.

This technique has evolved to include robust DEA to reduce the influence by the presence of outliers, and stochastic DEA since many observations in practice are stochastic in nature. It also enriches the analysis by speculating about the firms that have not been observed; the basic idea of which is that there would probably be an even more efficient benchmark firm if only more firms would have been observed. For our thought experiment, we used DEA in its deterministic form.

A fundamental assumption behind this method is that if a given firm A is producing Y(A) units of output with X(A) inputs, then the other firms should also be able to do the same if they were to operate efficiently. The problem facing the decision maker is to identify which of these branches are inefficient and the magnitude of the inefficiency. This information can be used to locate the branches that require remedial management actions, to reward the more efficient managers, and/or to determine the management techniques used in the more efficient branches that could be introduced into the less efficient branches.

Selecting inputs and outputs

DEA results are sensitive to the selection of inputs and outputs. As a result, the choice and the number of inputs and outputs determine how good of a discrimination exists between the efficient and inefficient firms. There are two orthogonal considerations when evaluating the size of the data set. One consideration is to include as many firms as possible because with a larger population there is a greater probability of capturing high performance units that would determine the efficient frontier and improve the discriminatory power. The other consideration with a large data set is that the homogeneity of the data set may decrease, meaning that some exogenous impacts of no interest to the analyst or beyond the control of the manager may affect the results.

For our thought experiment, we sought advice from industry experts on the variables that would most represent how a warehouse is benchmarked and these are shown as follows:

• Inputs: Space in m3, costs (equipment, system, facility, staff, energy)
• Outputs: Revenue or transfer pricing, throughput in m3, service level

We note that statistical experiments can be performed to improve parameter selection and welcome suggestions. Applying DEA Due to confidentiality, the results cannot be publicised. We will instead illustrate the findings based on the Transport and Storage Services Sector, with the data obtained from Statistics Singapore (see Table 1).

Table 1: DEA efficiency of the transport and storage services sector from 2013 to 2015

From Table 1, we observe that:

i. Storage For Class Cargo (SSIC 52103) has been the most efficient
ii. there is room for improvement for Shipping Lines, Branches Of Foreign Shipping Lines (SSIC 50021/2)
iii. the rapid decline of Taxi Booking Services, Towing Services, Supporting Services To Land Transport (SSIC 52212/13/19) could be an effect of ride-sourcing services
iv. there is some work to be done to improve General Warehousing (SSIC 52101)
v. Moving Services (SSIC 49232) and Sight-seeing Cruise & Passenger Ferry Services (SSIC 50011/2/3) have improved significantly


DEA provides for a quantitative and objective approach to measuring relative performance of firms participating a certain industry, and this performance can be improved from the cross-sharing of best practices across the set of firms through specific analyses of the input and output parameters. With LPI, companies based in Singapore now have the opportunity to explore better ways to improve their performance by understanding the different options in design and operations deployment that confer the greatest benefits.


The LPI Executive Committee would like to thank Supply Chain Asia for this learning opportunity. The committee is led by Professor Mark Goh, Department of Analytics and Operations, NUS Business School and supported by Sim Cheng Hwee, Chairman of Decision Solutions, and Laurent Simon, Managing Director (Singapore & Malaysia) of Kuehne + Nagel.

About the Author

Dr Koh Niak Wu is the founder of Cosmiqo International Pte Ltd, a supply chain and operational analytics firm. He is also an Adjunct Faculty of operations management at the Singapore Management University and Singapore University of Social Sciences. Prior to this, Niak Wu was responsible for the development and execution of data-driven strategic plans, and the transformation of logistics sourcing initiatives across Asia Pacific and Japan at Dell. At A*STAR, he was involved in planning and operations management with the goal of developing industries through analytical approaches.

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