There are two main dimensions in which the performance of a production unit can be assessed. The first is the dimension of time. The basic question here is: how is this or…
There are two main dimensions in which the performance of a production unit can be assessed. The first is the dimension of time. The basic question here is: how is this or that production unit doing over time? Assessing a unit's performance over time is called monitoring. The second dimension is characterized by the question: how is this or that production unit doing relative to other, similar units? To answer this question one needs to specify the reference set of units and one needs sufficient information on each of the members of this set. This activity is usually called benchmarking. A combination of the two dimensions in the setting of a panel is also possible.
The purpose of this paper is to present a refined framework providing clarity in terms of the components of profitability and productivity change from the perspective of…
The purpose of this paper is to present a refined framework providing clarity in terms of the components of profitability and productivity change from the perspective of the firm level.
The literature is analysed with a scoping study and a systematic literature review. Productivity measurement approaches are compared using data at the product level.
The definition of total factor productivity (TFP) in the literature negatively affects the accuracy of profitability and productivity measurement. In the usual case of a dynamic output mix, TFP change encompasses biasing output mix effects relating to profitability, but not to productivity change. Therefore, this paper defines changes of a ratio of output quantities to input quantities not as TFP change, but as quantitative profitability (QP) change. A framework is proposed decomposing profitability change into price recovery and QP change, whereas the latter comprises of valid productivity change (encompassing technological, technical efficiency and productivity-related scale effects) and output mix change (encompassing proportion, quality, output switching and profitability-related scale effects).
Future research should include literature from the industrial organisation field of economics. The presented framework should be transferred to the standard production function framework used in economics.
The paper can help preventing faulty decision making or distrust due to the use of biased profitability or productivity indicators. TFP-based productivity indicators are unsuitable for most firms. To measure productivity meaningfully, firms should use adequate approaches (e.g. standard input- or adjusted total factor productivity-based ones).
The paper contributes to a more accurate performance measurement approach, as researchers and practitioners better understand the components of profitability and productivity change.
The purpose of this paper is to compare efficiency scores from the benchmarking exercise with those of previous studies and to discuss the reasons behind diverging results.
This paper uses data envelopment analysis (DEA) on primary data of large container terminals.
The results differ strongly from those available in the literature. Causes for these differences are: public (secondary) data are not always accurate; different terminal types are compared; terminals of different scale are compared; and terminals are mixed with ports.
DEA may be appropriate for container terminal benchmarking, but only if better quality and additional input and output data can be obtained. In its application, the analysis should be controlled for terminal types.
Summary of the state of play in the use of DEA methodologies for comparing the efficiency of container terminals at ports.
The chapter reviews and extends the theory of exact and superlative index numbers. Exact index numbers are empirical index number formula that are equal to an underlying…
The chapter reviews and extends the theory of exact and superlative index numbers. Exact index numbers are empirical index number formula that are equal to an underlying theoretical index, provided that the consumer has preferences that can be represented by certain functional forms. These exact indexes can be used to measure changes in a consumer's cost of living or welfare. Two cases are considered: the case of homothetic preferences and the case of nonhomothetic preferences. In the homothetic case, exact index numbers are obtained for square root quadratic preferences, quadratic mean of order r preferences, and normalized quadratic preferences. In the nonhomothetic case, exact indexes are obtained for various translog preferences.
- exact index numbers
- superlative index numbers
- flexible functional forms
- Fisher ideal index
- normalized quadratic preferences
- mean of order r indexes
- homothetic preferences
- nonhomothetic preferences
- cost of living indexes
- the measurement of welfare change
- translog functional form
- duality theory
- Allen quantity index
The purpose of this paper is to investigate how warehouse management, understood as a cluster of planning and control decisions and procedures, is organized and driven by task complexity (TC) and market dynamics (MD).
A multi‐variable conceptual model is developed based on the literature and tested among 215 warehouses using a survey.
The results suggest that TC and MD are the main drivers of warehouse management, measured by planning extensiveness (PE), decision rules complexity, and control sophistication. Differences between production and distribution warehouses are found with respect to the relationship between assortment changes and PE. Furthermore, TC appears to be a main driver of the specificity of the warehouse management (information) system (WMS).
This paper is based on 215 warehouses in The Netherlands and Flanders (Belgium); future research may test the model on a different sample. More research should be conducted to further validate the measures of the core dimensions of warehouse management.
Different levels of TC and MD characterize warehouses. Such a characterization is a first step in determining generic warehouse functionalities and helping managers to decide on the best software for their warehouse operations.
The paper defines the core dimensions of warehouse management, makes them measurable, tests them and assesses how these drivers impact specificity of WMS. The paper shows that PE in production warehouses is driven by different variables than in distribution centers.