Modelling requirements of supplier selection for benchmarking service provision

Supplier selection has a great impact on supply chain management. The quality of supplier selection also affects profitability of organisations which work in the supply chain. As suppliers can provide variety of services and customers demand higher quality of service provision, the organisation is facing challenges for making the right choice of supplier for the right needs. The existing methods for supplier selection, such as data envelopment analysis (DEA) and analytical hierarchy process (AHP) can automatically perform selection of competitive suppliers and further decide winning supplier(s). However, these methods are not capable of determining the right selection criteria which should be derived from the business strategy. An ontology model described in this paper integrates the strengths of DEA and AHP with new mechanisms which ensure the right supplier to be selected by the right criteria for the right customer's needs.

Benchmarking and valuation issues in measuring depreciation for European office markets

Purpose – The paper addresses the practical problems which emerge when attempting to apply longitudinal approaches to the assessment of property depreciation using valuation-based data. These problems relate to inconsistent valuation regimes and the difficulties in finding appropriate benchmarks. Design/methodology/approach – The paper adopts a case study of seven major office locations around Europe and attempts to determine ten-year rental value depreciation rates based on a longitudinal approach using IPD, CBRE and BNP Paribas datasets. Findings – The depreciation rates range from a 5 per cent PA depreciation rate in Frankfurt to a 2 per cent appreciation rate in Stockholm. The results are discussed in the context of the difficulties in applying this method with inconsistent data. Research limitations/implications – The paper has methodological implications for measuring property investment depreciation and provides an example of the problems in adopting theoretically sound approaches with inconsistent information. Practical implications – Valuations play an important role in performance measurement and cross border investment decision making and, therefore, knowledge of inconsistency of valuation practice aids decision making and informs any application of valuation-based data in the attainment of depreciation rates. Originality/value – The paper provides new insights into the use of property market valuation data in a cross-border context, insights that previously had been anecdotal and unproven in nature.

Benchmarking and setting intervention targets for key cell count parameters

Herd Companion uses routine milk‐recording records to generate twelve‐month rolling averages that indicate performance trends. This article looks at Herd Somatic Cell Count (SCC) and four other SCC‐related parameters from 252 National Milk Records (NMR) recorded herds to assess how each parameter correlates with the Herd SCC. The analysis provides evidence for the importance of targeting individual cows with high SCC recordings (>200,000 cells/ml and >500,000 cells/ml) and/or individual cows with repeatedly high SCC recordings (chronic high SCC) and/or cows that begin lactation with a high SCC recording (dry period infection) in order to achieve bulk milk Herd SCC below 200,000 cells/ml.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

Benchmarking energy use in licensed restaurants and pubs

Commercial kitchens often leave a large carbon footprint. A new dataset of energy performance metrics from a leading industrial partner is presented. Categorising these types of buildings is challenging. Electricity use has been analysed using data from automated meter readings (AMR) for the purpose of benchmarking and discussed in terms of factors such as size and food output. From the analysed results, consumption is found to be almost double previous sector estimates of 6480 million kWh per year. Recommendations are made to further improve the current benchmarks in order to attain robust, reliable and transparent figures, such as the introduction of normalised performance indicators to include kitchen size (m2) and kWh per thousand-pound turnover.

Energy benchmarking in commercial kitchens

Commercial kitchens are one of the most profligate users of gas, water and electricity in the UK and can leave a large carbon footprint. It is estimated that the total energy consumption of Britain’s catering industry is in excess of 21,600 million kWh per year. In order to facilitate appropriate energy reduction within licensed restaurants, energy use must be translated into a form that can be compared between kitchens to enable operators to assess how they are improving and to allow rapid identification of facilities which require action. A review of relevant literature is presented and current benchmarking methods are discussed in order to assist in the development and categorisation of benchmarking energy reduction in commercial kitchens. Energy use within UK industry leading brands is discussed for the purpose of benchmarking in terms of factors such as size and output.

A comprehensive benchmarking system for evaluating global vegetation models.

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and 5 height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, 10 and are compared to scores based on the temporal or spatial mean value of the observations and a “random” model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global 15 vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the interannual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified 20 several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change 25 impacts and feedbacks.

eRPUK 2003: eCommerce and Retail Property in the UK: third annual benchmarking survey

This new survey, which has just been completed and includes brand new data, has been funded by the RICS Education Trust and the European Shopping Centre Trust. It follows up our 2000 survey of UK retailers, investors and developers. The report presents results from our new 2001 survey. This continuing benchmark series of studies includes an extensive review of developments in ecommerce and retail in Europe and the USA. The survey reveals a cooling in attitude towards ecommerce in the UK, but there is rapid growth in some sectors and polarisation and marginalisation of secondary centres are likely to increase. In Europe the growth of a three tier system of ecommerce 'pioneers', 'followers' and 'laggards' is becoming established, and the research also reveals results from a recent joint survey on US and UK retailers conducted with Colorado State University. There is a danger of complacency as UK online sales (in percentage terms) now outstrip USA.

Climate model benchmarking with glacial and mid-Holocene climates

Past climates provide a test of models’ ability to predict climate change. We present a comprehensive evaluation of state-of-the-art models against Last Glacial Maximum and mid-Holocene climates, using reconstructions of land and ocean climates and simulations from the Palaeoclimate Modelling and Coupled Modelling Intercomparison Projects. Newer models do not perform better than earlier versions despite higher resolution and complexity. Differences in climate sensitivity only weakly account for differences in model performance. In the glacial, models consistently underestimate land cooling (especially in winter) and overestimate ocean surface cooling (especially in the tropics). In the mid-Holocene, models generally underestimate the precipitation increase in the northern monsoon regions, and overestimate summer warming in central Eurasia. Models generally capture large-scale gradients of climate change but have more limited ability to reproduce spatial patterns. Despite these common biases, some models perform better than others.

A comprehensive benchmarking system for evaluating global vegetation models

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

The development of a data-driven application benchmarking approach to performance modelling

Performance modelling is a useful tool in the lifeycle of high performance scientific software, such as weather and climate models, especially as a means of ensuring efficient use of available computing resources. In particular, sufficiently accurate performance prediction could reduce the effort and experimental computer time required when porting and optimising a climate model to a new machine. In this paper, traditional techniques are used to predict the computation time of a simple shallow water model which is illustrative of the computation (and communication) involved in climate models. These models are compared with real execution data gathered on AMD Opteron-based systems, including several phases of the U.K. academic community HPC resource, HECToR. Some success is had in relating source code to achieved performance for the K10 series of Opterons, but the method is found to be inadequate for the next-generation Interlagos processor. The experience leads to the investigation of a data-driven application benchmarking approach to performance modelling. Results for an early version of the approach are presented using the shallow model as an example.

BnP1, a novel P-I metalloproteinase from Bothrops neuwiedi venom: Biological effects benchmarking relatively to jararhagin, a P-IIISVMP

Snake venom metalloproteinases (SVMPs) have been extensively studied and their effects associated with the local bleeding observed in human accidents by viper snakes. Representatives of P-I and P-III classes of SVMPs similarly hydrolyze extracellular matrix proteins or coagulation factors while only P-III SVMPs induce significant hemorrhage in experimental models. In this work, the effects of P-I and P-III SVMPs on plasma proteins and cultures of muscle and endothelial cells were compared in order to enlighten the mechanisms involved in venom-induced hemorrhage. To reach this comparison, BnP1 was isolated from B. neuwiedi venom and used as a weakly hemorrhagic P-I SVMPs and jararhagin was used as a model of potently hemorrhagic P-III SVMP. BnP1 was isolated by size exclusion and anion-exchange chromatographies, showing apparent molecular mass of approximately 24kDa and sequence similarity with other members of SVMPs, which allowed its classification as a group P-I SVMP. The comparison of local effects induced by SVMPs showed that BnP1 was devoid of significant myotoxic and hemorrhagic activities and jararhagin presented only hemorrhagic activity. BnP1 and jararhagin were able to hydrolyze fibrinogen and fibrin, although the latter displayed higher activity in both systems. Using HUVEC primary cultures, we observed that BnP1 induced cell detachment and a decrease in the number of viable endothelial cells in levels comparable to those observed by treatment with jararhagin. Moreover, both BnP1 and jararhagin induced apoptosis in HUVECs while only a small increase in LDH supernatant levels was observed after treatment with jararhagin, suggesting that the major mechanism involved in endothelial cell death is apoptosis. Jararhagin and BnP1 induced little effects on C2C12 muscle cell cultures, characterized by a partial detachment 24h after treatment and a mild necrotic effect as evidenced by a small increase in the supernatants LDH levels. Taken together, our data show that P-I and P-III SVMPs presented comparable effects except for the hemorrhagic activity, suggesting that hydrolysis of coagulation factors or damage to endothelial cells are not sufficient for induction of local bleeding. (C) 2007 Elsevier Ltd. All rights reserved.

Estudo para identificação de Benchmarking em sistemas de produção de leite no Rio Grande do Sul

A desregulamentação da economia brasileira na cadeia do leite em 1990 e a globalização da economia trouxeram grandes modificações e preocupações nos diferentes segmentos do sistema agroindustrial do leite. De outro lado, surgiu um consumidor mais exigente em produtos e serviços. Essa situação requer do produtor de leite, que é o elo mais frágil da cadeia, que tenha maior competitividade em produtividade, custo e qualidade, dentre outros fatores. Nesse sentido, este estudo analisa e avalia os coeficientes de desempenho dos indicadores técnico e econômico dos fatores da produção que caracterizam os diferentes sistemas de produção e identificam as unidades produtoras que são benchmarking em alguma prática, processo, procedimento ou indicador. Para tanto, realizou-se uma pesquisa a campo que obteve um retorno de 47,78% dos 32.188 questionários enviados. Além disso, realizou-se visita, entrevista e estudadas as 12 UPs benchmarking. Observou-se que as UPs benchmarking têm muito a ensinar à grande massa de produtores de leite e que todos os coeficientes dos indicadores técnicos e econômicos, práticas, processos e procedimentos são melhores do que a média de cada sistema da pesquisa geral.