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The purpose of this paper is to formulate the process of measuring and benchmarking the performance of knowledge management (KM) practices as a multicriteria group decision-making problem and present a new multicriteria group decision-making approach for effectively evaluating the performance of KM practices to meet the interests of various stakeholders in small and medium enterprises (SMEs).

A new multicriteria group decision-making approach is developed for evaluating the performance of KM practices of individual SMEs. Intuitionistic fuzzy numbers are used for representing the subjective assessments of decision makers in evaluating the relative importance of the evaluation criteria and the performance of individual KM practices with respect to specific evaluation criteria. A fuzzy multicriteria group decision-making algorithm is developed for measuring and benchmarking the performance of alternative KM practices.

The proposed multicriteria group decision-making approach is capable of effectively evaluating the performance of KM practices through adequately considering the presence of multiple decision makers, the multi-dimensional nature of the evaluation problem, and appropriately modeling the subjectiveness and imprecision of the evaluation process. The presentation of an example shows that the proposed fuzzy multicriteria group decision-making algorithm is simple to use and efficient in computation.

The outcome of the multicriteria group decision-making approach is highly dependent on the inputs provided by the decision maker.

The novelty from this research lies in the utilization of a multicriteria group decision-making approach for evaluating the performance of KM practices in an organization. The outcome from the performance evaluation process allows the enterprise to adopt appropriate KM practices for achieving competitive advantages.

The proposed multicriteria group decision-making approach has a significant social implication as it can be used as a decision-making tool for providing various decision makers in SMEs with useful and strategic information concerning the performance of KM practices in a given situation.

The originality of this paper lies in the development of the multicriteria group decision-making approach for effectively measuring and benchmarking the performance of KM practices of individual SMEs.

The purpose of this paper is to introduce a new forecasting approach that involves a multicriteria scoring model, which is enhanced with regression analysis and optimization. We compare regression analysis versus our Enhanced Multicriteria Scoring Model by comparing the Error Sum of the Squares in case studies involving top selling automobiles and top Fortune 500 companies. In both the automobile and Fortune 500 case studies, our Enhanced Multicriteria Scoring was more accurate than regression analysis. In practice, our Enhanced Multicriteria Scoring Model should be compared with regression analysis, and the better of the two techniques should be used to forecast. In short, our Enhanced Multicriteria Scoring model is a “breakthrough” modeling technique that will help companies and organizations improve their forecasting.

The business performances of firms are measured on a set of indicators (Financial Ratio Analysis Indicators or Balanced Scorecard Key Performance Indicators). Traditional benchmark analysis considers a set of criteria, though it generally synthesizes all the results, taking into consideration only an aggregate performance criterion (reductio ad unum approach). This methodology has many disadvantages, both theoretical and empirical. The purpose of this paper is to illustrate the advantages, in terms of greater flexibility and realism, related to the application of a multicriteria methodology.

The paper uses a tutorial approach. An exemplification of an outranking multicriteria methodology (ELECTRE type) is described.

The main findings of the paper can be summarized as: first, the evaluation of a business performance cannot generally be conducted by means of a unique criterion as in the traditional monocriterion benchmark analysis; second, when the evaluation of a firm is based on different genuine criteria, the performance has to be “satisfacing” and not maximizing; and third, the outranking methods are able to provide logically rigorous solutions to the genuine multicriteria benchmarking evaluation problems.

The paper provides practical implications useful for evaluating firm performances in many cases, also when each stakeholder (managers, shareholders, banks, etc.) assigns different “weights” to the decision criteria.

As a multicriteria evaluation is generally incompatible with a profit maximizing approach, the paper proposes a multicriteria performance measure approach that offers Simon's satisfacing solutions. The paper shows that satisfacing solutions to a multicriteria evaluation problem may be rigorously obtained through an outranking methodology (already introduced by other scholars).

In a previous paper the authors emphasized the advantages of multicriteria methodologies to evaluate business performance. The purpose of this paper is to highlight the metachoice problem that always arises in a benchmark multicriteria analysis that can be synthesized as follows: “how to choose an algorithm to choose?”

In order to perform a benchmark analysis, a set of criteria must be chosen. In the Balanced Scorecard approach, for example, key performance indicators (KPIs) are grouped in four different perspectives: financial, customer, internal processes and learning and growth. In this paper, the authors focus on multicriteria benchmark analysis applied to KPIs of the financial perspective. The paper considers a set of criteria used in financial statement analysis based on balance sheet, income statement and cash flow statement. A case study is described.

The main findings of the paper are when the evaluation of a firm is based on different genuine criteria, a metachoice problem arises: multicriteria ranking algorithms cannot be selected using a multicriteria algorithm; the choice of an algorithm ultimately depends on the subjective preference of the policy maker; and the authors metachoice solution to the benchmarking problem is in accordance with Simon’s satisfacing solution, describing a non-maximizing performance measurement methodology.

The paper provides several practical implications in all cases in which a ranking has to be assigned to a group of firms based on financial performances. More in general the problem is very relevant when a ranking has to be carried out with respect to a set of projects, a set of strategies, a set of organizational units, etc.

The adoption of a set of criteria is certainly an advantage to avoid uni-criterial myopic evaluation. However, this also creates some methodological problems. The paper demonstrates the “relativity” (subjectivity) of results of the evaluation process when there are many evaluation criteria, as in a benchmark context. This is a metachoice problem that cannot be solved by using another multicriteria algorithm.

The purpose of this paper is to present the multicriteria identification method used for solving the microscale heat transfer problem. The thin film exposed to ultrashort laser pulse is modeled using the finite difference method. The parameters of the model are tuned on the basis of experimental data. The multicriteria identification of the numerical model parameters is performed for subsets of experimental data.

The multicriteria identification method is used in the paper. The Pareto front for two criterions is created. The two-temperature model of heat transfer in microscale is used in the numerical model.

The multicriteria identification for two subsets of experimental data leads to different results. The obtained Pareto front allows to choose the most suitable set of numerical model parameters.

The multicriteria identification method was used for the first time to solve the microscale heat transfer problem.

The purpose of this paper is to present a review of the main factors that are important to consumers of dairy products and develop a new product performance index for evaluating and benchmarking the performance of dairy products. This helps dairy product companies understand their dairy products’ overall performance level, relative to other dairy products in the market.

A new product performance index is developed for evaluating and benchmarking the performance of dairy products with respect to multiple criteria. Seven important criteria are identified for evaluating and benchmarking the performance of dairy products. To deal with the subjective assessments of qualitative performance measures, linguistic terms approximated by fuzzy numbers are used. Based on the concept of the degree of dominance, a fuzzy multicriteria group decision-making approach is developed to obtain a product performance index for each dairy product.

The proposed multicriteria group decision-making approach is found to be useful and effective in evaluating and benchmarking the performance of dairy products. The approach is capable of adequately considering the presence of multiple decision makers, the multi-dimensional nature of the performance evaluation problem, and modeling the subjectiveness and imprecision of the performance evaluation process.

The outcome of the multicriteria group decision-making approach is dependent on the subjective inputs provided by the decision makers.

This product performance index will provide useful insights for companies understand their strengths and weaknesses in terms of their products’ performance criteria, and identify relevant areas for continuous improvement. This product performance index is also applicable for dealing with the general multicriteria decision-making problems.

The proposed multicriteria group decision-making approach can be used as a decision-making tool for providing various decision makers in dairy product companies and general consumers with useful information regarding the performance of different dairy products.

This paper highlights the important factors for evaluating and benchmarking dairy products and develops a new product performance index for evaluating and benchmarking the performance of dairy products in China.

In continuation of the recent development of Evolutionary Structural Optimisation (ESO) applied to the simultaneous objective to maximise the natural frequency and to minimise the mean compliance, presents the Multicriteria ESO optimisation of two new criteria. This has been done with the use of four different multicriteria methods. Three examples have been used to verify the usefulness and capability of these methods applied to ESO in the context of the aforementioned criteria. Concluded that the ESO weighting method is proficient in presenting the designer with a range of options (of Pareto attribute) taking into account multiple criteria, and the global criterion method has the tendency to produce shapes and topologies that resemble that of the weighted 50 per cent: 50 per cent method. Likewise, the logical OR operator method produced designs that corresponded directly to those of 100 per cent stiffness weighted criteria. No clear resemblance could be concluded with the case of the logical AND operator method.

To provide decision makers (DMs) an option for addressing problems involving finite alternative sets and multiple criteria, where criterion weighting is difficult or impossible.

The multicriteria decision problem is described, and a typically descriptive (rather than prescriptive) tool, data envelopment analysis (DEA), is summarized, along with a hypothetical but typical example of a multicriteria decision (vendor selection). The DEA approach is modified to incorporate weight constraints and is used to rank the available vendors. Results are compared with those from the use of a popular multicriteria decision tool (SMART) and a naïve averaging approach.

The modified DEA approach yields results very similar to those produced using SMART; these results are quite satisfactory in spite of the fact that DEA requires less involvement on the part of the DM. In addition, non‐dominant optima (a possible anomaly with DEA) are avoided, and often a single alternative, rather than a non‐dominated set, will result, thus providing a unique optimum.

Results are based on the analysis of a single data set. Future investigation should examine the performance of the DEA approach when other data sets involving more like as well as more unlike alternatives are involved.

With DEA the burden on the DM is reduced, as the need for eliciting criterion weights is obviated. DEA should thus provide an acceptable alternative to prescriptive modeling tools when multiple DMs are involved and/or criterion weight determination is unfeasible.

This paper demonstrates how DEA, a tool used more typically in post hoc evaluations, can be used also, with some modifications, as a prescriptive decision support tool.

The purpose of this paper is to observe how to invest in upper-middle income countries via an innovation perspective following global innovation index (GII) by multicriteria decision aid (MCDA) approach, once MCDA was designed to support subjective decisions.

Pearson’s correlation was the milestone for understanding innovation indicators at upper-middle income countries profiles. In a MCDA first step, the analytical hierarchy process (AHP) was applied to obtain the criteria weight. In this step, the judgments or evaluations inputted in AHP were collected from a sample composed by five experts in GII. After getting the criteria weights compose to GII, Borda and Preference Ranking Organization Method for Enrichment Evaluations (PROMÉTHÉE) methods were applied to obtain an MCDA-based GII. The inputs for this second step were: the weights come from AHP output; and the countries performance came from GII data.

As a result, it was found out the upper-middle countries’ rank to invest and groups with countries acting like “hubs” or “bridges” for economic sectors in near countries; when they are grouped according to their maximum and minimum scores profiles, observing not only a particular region but also similar profiles at diverse world areas.

Pearson-AHP-PROMÉTHÉE works as a supportive decision tool for several and complex investment perspectives from criteria and alternatives analysis regarding innovation indicators for upper-middle income countries. This combination also demonstrates grouping possibilities, aligning profiles and not only ranking countries for investment and eliminating others but also grouping countries with similar profiles via innovation indicators MCDA combined application.

The benchmarking analysis of organizational learning capability considers a set of criteria: clarity of purposes and mission, leadership commitment and empowerment, experimentation and rewards, transfer of knowledge, teamwork of group problem solving, etc. For this reason it assumes the configuration of a multicriteria analysis. In the traditional benchmarking the multicriteria problem is solved throughout the construction of a synthetic indicator obtained by averaging all scores assigned to an organization on the different criteria. This methodology presents many theoretical and empirical disadvantages. This paper illustrates the advantages, in terms of greater flexibility and realism, connected to the application of the multicriteria methodology founded on the notion of outranking. In fact, such a methodology solves the multicriteria benchmarking problem without using the averaging rule adopted by the traditional benchmarking approach.