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This case study aims to examine the role of demand management in balancing distribution efficiency and responsiveness to customer needs in the downstream of a retail supply chain.

A major machine part supplier in Australia is used as a case study to investigate the challenges faced by the industry in distributing goods to customers. The use of demand management techniques to help improve distribution efficiency without significantly impacting on responsiveness is also explored.

The findings of the case study reveal that appropriate demand management measures, such as customer segmentation and price discrimination, can help improve overall distribution efficiency of the supply chain while providing the required responsiveness to meet genuine customer needs. Other management attempts, such as vendor‐managed inventory and rationalisation of retail network, can facilitate demand aggregation and improve vehicle utilisation in distribution with minor impact on customer service. These changes require a full understanding of customer requirements and supply capabilities of the company as well as corresponding adjustments in business strategy, leadership style, and organisational culture.

This study lends insight into the use of demand management techniques to improve efficiency in downstream wholesale and retail distribution, thereby enhancing sustainability and profitability of business. To serve mainly as a case study and an illustration of the approach, the scope of the study is limited to six stores in the distribution network of the case company.

Retailers can explore the use of demand management techniques to increase distribution efficiency and hence competitiveness of the company. The approach can also assist managers in adopting best practices among stores and facilitate more effective allocation of distribution resources to serve different market segments.

Using demand management techniques to increase distribution efficiency can reduce delivery frequency and total travel distance. This will help lessen energy usage, carbon emission, traffic congestion, and other negative impacts on the environment.

Research in retail distribution efficiency to date focuses mainly on delivery optimisation through routing and scheduling. Attempts to link demand with supply and use demand management techniques to improve distribution efficiency are relatively limited. This paper fills the gap in the literature by investigating the value of demand management in distribution and explores empirically the significance of the approach to achieve higher wholesale and retail distribution efficiency.

The objective of the present study is to evaluate and analyse the performance of Indian electricity distribution utilities post the implementation of landmark Electricity Act 2003.

Stochastic frontier analysis (SFA) that incorporates exogenous influences on operational efficiency is adopted in the present study. Specifically, a stochastic frontier production function model with a technical inefficiency effects model (Battese and Coelli, 1995) is chosen as a preferred model. In this model, the function that explains the inefficiency scores is estimated in a single stage with the production technology. This avoids the problem of inconsistency which is possible in the two-stage approach.

The sample involved 52 Indian electricity distribution utilities for seven-year period from 2006 to 2013. Major findings of SFA show that Indian electricity distribution utilities post the implementation of Electricity Act (2003) had, on average, experienced efficiency improvement during the observed period. The overall mean technical effciency score is estimated as 78.5% which indicates that there exist wide scope for effciency improvement in the sector. Further, the empirical findings also indicate that publicly owned distribution utilities obtain average technical efficiencies of 71.3%, which is lower than privately owned distribution utilities, which achieve average technical efficiencies of 85.7%.

Power supply quality indicators such as SAIFI, SAIDI, CAIFI, etc. and unobserved heterogeneity also influence the efficiency analysis of electricity distribution utilities. Hence, these parameters as explanatory variables can be incorporated in the future work.

The results obtained from this empirical study would likely be helpful for utility managers and policymakers to know how well they are performing, and how a better corporate strategy a particular utility can formulate to improve its operational efficiency and also its position in the marketplace.

This paper is amongst the first significant attempts that implement SFA approach to the panel dataset over a longer period of time – 2006 to 2013, so, as to evaluate and analyse the operational efficiency of Indian electricity distribution utilities in a single framework after the enactment of Electricity Act (2003). Unlike previous studies, this study investigates the degree to which various exogenous (or environmental) factors influence efficiency levels in these utilities.

The purpose of this paper is to discuss the use of data envelopment analysis (DEA) to benchmark store performance for the purpose of rationalising retail distribution network.

As an illustration of the approach, DEA is applied to a sample of front stores of a major retailer in Australia to compare their relative efficiency in distribution. Together with other techniques such as customer segmentation and spatial distribution of demand, this paper shows that DEA can provide an objective basis for distribution network rationalisation and be a suitable analytical tool to facilitate continuous improvement.

Based on the DEA results, it is concluded that overall distribution efficiency of the part of the retail network under study can be improved by either closing the less efficient stores or merging them with the others in the same service areas to streamline the network. Such rationalisation will help aggregate demand and improve vehicle utilisation for distribution with minor impact on current level of customer service.

This study lends insight into the use of DEA, together with other analyses, for distribution network rationalisation. This approach is less data hungry and relatively easy to implement than full‐fledged optimisation through integer programming. To serve mainly as a proof of concept and an illustration of the approach, the scope of the study is limited to six stores in the retail network with relative performance in distribution evaluated on a single input and a single output variables.

Managers can use DEA to benchmark the distribution performance of their stores against the best performers in the retail network so as to identify areas for improvement. The approach can also assist in the adoption of best practice and facilitate more effective allocation of resources across the entire retail network.

Retail network rationalisation through benchmarking with DEA can facilitate continuous improvement in distribution efficiency. This will help reduce fuel consumption, carbon emission, as well as other pollutions such as noise and traffic congestion.

Research in retail network performance using DEA to date is mainly on comparative performance of supermarkets within or between chains. The focus is mainly placed on the relationship between floor area, workforce, and sales. This paper fills the gap in the literature by applying DEA in distribution network rationalisation instead of mere performance comparison of individual stores. It focuses on distribution costs rather than store attributes and supplements DEA with other techniques to obtain a fuller picture of the overall network efficiency in terms of distribution. It also contributes to a better understanding of how demand management can affect distribution efficiency of the retail network.

This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran’s 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions.

The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model’s robustness.

The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels.

By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions.

The paper’s novel concept applies DEA to Iran’s electricity distribution companies and analyzes them by examining geographic classification, size and the type of the companies. In addition, a stability test is conducted and productivity changes are estimated.

The paper aims to research the capability of using the tools of domestic and international performance benchmarking for estimation of efficiency, determine its key factors and reveal the full reserves of inputs reduction and potential growth efficiency for gas distribution companies.

In the research, three models of data envelopment analysis (DEA) and other tools of performance benchmarking are used to analyse the efficiency of gas distribution companies. Some factors are examined for their impact on efficiency. There are the scale, regional location, property category and other endogenous and exogenous factors. The results are based on the samples of 54 Ukrainian and 20 US gas distribution companies.

International benchmarking vastly expand performance improvement for domestic companies. The results of analysis and tools of this research allows to widen the capability of reduced consumption of various inputs by 10 per cent and provide the general gain of Ukrainian gas distribution company's and industry efficiency two times as much.

The research is limited by single industry and by relatively short data set. The former is explained by requirement of technology (product and service) homogeneity when using DEA tools. The latter is connected with specificity of the industry and generally little numbers of firms in it.

The results of researching contain the data and recommendations to optimal scale, best proprietors makeup, specific reserves of input reduction for each company that was analysed. These results can be practicable for companies' management, present and potential investors and proprietors, regulative public authority. It is possible to use results of this research to make benchmarking for the other industries.

This is the first paper that adopts the various DEA models for measuring of efficiency in gas distribution industry of Ukraine and the tools of international benchmarking for Ukrainian and US companies.

The purpose of this paper is to analyze the impact on Spanish retail firms' efficiency of the regulatory process experienced in the period 1994‐2002. In particular, the paper aims to study the influence of the Retail Trade Act of 1996, by means of which the Spanish state transferred authority to concede licenses for opening commercial establishments to the regions.

The objectives have been achieved by non parametric methodology of analysis of efficiency at static and dynamic level (kernel density).

The results confirm a decrease in firms' technical efficiency in the post‐regulatory period between 1996 and 2002. The different patterns of convergence/divergence in the distribution of technical efficiency depend on firm size. Small firms experience a process of divergence to lower efficiency levels from 1994 to 2002.

Perhaps more regional analysis is also needed. In this sense, it would be interesting to consider the region within which the firms operate, and hence take into account the differences in regulatory processes in each region by size or more precisely by outlet type.

It is necessary to consider the implications that the regulatory processes can have for management in the environment of the retail industry.

The analysis of the efficiency with features of density kernel. As well as the usage of stochastic kernel. This has enabled dynamic effects in the analysis.

In today’s multi-channel distribution environment, it is crucial for the competitiveness of retail stores and companies to improve efficiency of dedicated logistics centers (DLCs) servicing types of retail outlets such as large retail-stores, super supermarkets, and convenient stores. This paper derives efficiency improvement strategies for DLCs through efficiency analyses. To this end, this paper empirically analyzes the efficiency of DLCs by applying DEA (data envelopment analysis) approaches using real data from a Korean distribution company. In detail, this paper analyzes operations, finance, and service efficiencies of DLCs and performs a comparison analysis on the efficiency between DLCs. Finally, this paper discusses the analysis results considering the DLCs’ characteristics and derives managerial and operational implications.

The purpose of this paper is to investigate coexistence of multiple distribution systems in property‐casualty (P/C) insurance industry in the USA.

Stochastic frontier analysis is used to measure cost and revenue efficiencies of P/C insurance companies utilizing different distribution systems.

Independent agent insurers are found to be cost inefficient compared to insurers with other distribution systems, but the independent agent insurers have better revenue efficiency compared to their long counterpart, the exclusive agent insurers. This study also documents that the direct writing system provides higher cost and revenue efficiencies than other distribution systems, although their efficiencies have been deteriorating during the same time period.

Future research could examine whether the findings change by measuring efficiencies with a non‐parametric method, i.e. data envelopment analysis.

A start‐up insurer should consider a direct writing system, which is most cost and revenue efficient.

This paper investigates efficiencies of insurers by four different distribution systems and tracks efficiency changes of insurers over 12‐year periods.

The purpose of this paper is to contribute to the empirical evidence about crop yield distributions that are often used in practical models evaluating crop yield risk and insurance. Additionally, a simulation approach is used to compare the performance of alternative specifications when the underlying form is not known, to identify implications for the choice of parameterization of yield distributions in modeling contexts.

Using a unique high-quality farm-level corn yield data set, commonly used parametric, semi-parametric, and non-parametric distributions are examined against widely used in-sample goodness-of-fit (GOF) measures. Then, a simulation framework is used to assess the out-of-sample characteristics by using known distributions to generate samples that are assessed in an insurance valuation context under alternative specifications of the yield distribution.

Bias and efficiency trade-offs are identified for both in- and out-of-sample contexts, including a simple insurance rating application. Use of GOF measures in small samples can lead to inappropriate selection of candidate distributions that perform poorly in straightforward economic applications. The β distribution consistently overstates rates even when fitted to data generated from a β distribution, while the Weibull consistently understates rates; though small sample features slightly favor Weibull. The TCMN and kernel density estimators are least biased in-sample, but can perform very badly out-of-sample due to overfitting issues. The TCMN performs reasonably well across sample sizes and initial conditions.

Economic applications should consider the consequence of bias vs efficiency in the selection of characterizations of yield risk. Parsimonious specifications often outperform more complex characterizations of yield distributions in small sample settings, and in cases where more demanding uses of extreme-event probabilities are required.

The study helps provide guidance on the selection of distributions used to characterize yield risk and provides an extensive empirical demonstration of yield risk measures across a high-quality set of actual farm experiences. The out-of-sample examination provides evidence of the impact of sample size, underlying variability, and region of the probability measure used on the performance of candidate distributions.

European paper industry has been struggling with margins and profitability for more than decade time period. At typical in markets of west, paper product demand is at long-term decline, mostly driven by continuously increasing internet use. However, in emerging markets demand still exists, and in Europe numerous small markets in east have even some growth available. The paper aims to discuss these issues.

The authors analyse in this research work with longitudinal data (period of 2002-2009) from one large Finnish paper mill and data envelopment analysis (DEA) approach, how distribution efficiency to selected eight East European markets has evolved.

In general distribution efficiency has improved, but this has taken place in step-wise manner rather than being linear year-to-year development (year 2006 found to be the threshold). Reason is mostly in better management of transportation costs, and in particular lower monthly deviation of these costs. It is surprising that case paper mill has been able to manage transportation costs in rapidly increasing energy cost environment so efficiently. Maybe European Union enlargement of 2004 and 2007 has had its effects on distribution efficiency.

The research is limited to the deliveries of one paper mill located in Finland. Also East European markets in the early periods of this study were emerging papers markets, and distribution practices were clearly evolving.

Based on the study East European paper market distribution should give more attention on transportation cost control, and trying to find solutions to minimize it with low monthly fluctuation.

Very few studies exist from East European distribution issues, and particularly that of paper industry. Also used quantitative method of DEA is relatively new in this context and gives valuable insights for the distribution efficiency development.