Purpose The purpose of this paper is to propose a decision support system (DSS) that stabilizes the flow of fixtures in manufacturing systems. The proposed DSS assists decision-makers to reuse or adapt the available fixtures or to manufacture new fixtures depending upon the similarity between the past and new cases. It considers the cost effectiveness of the proposed decision when an adaptation decision is passed. Design/methodology/approach The research problem is addressed by integrating case-based reasoning, rule-based reasoning and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them by their feature vectors. The fuzzy analytic hierarchy process (FAHP) and the inverse of weighted Euclidean distance measure are applied for case retrieval. A machining operation is illustrated as a computational example to demonstrate the applicability of the proposed DSS. Findings The problems of fixture assignment and control have not been well-addressed in the past, although fixture management is one of the complex problems in manufacturing. The proposed DSS is a promising approach to address such kinds of problems using the three components of an artificial intelligence and FAHP. Research limitations/implications Although the DSS is tested in a laboratory environment using a numerical example, it has not been validated in real industrial systems. Practical implications The DSS is proposed in terms of simple rules and equations. This implies that it is not complex for software development and implementation. The illustrated numerical example indicates that the proposed DSS can be implemented in the real-world. Originality/value Demand-driven fixture retrieval and manufacture to assign the right fixtures to planned part-orders using an intelligent DSS is the main contribution. It provides special consideration for the adaptation of the available fixtures in a system.

Purpose This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing. Design/methodology/approach The existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework. Findings Recent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items. Research limitations/implications The theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future. Originality/value The proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

Purpose: The purpose of this paper is to investigate the relationship between multi-criteria performance measurement (MCPM) practice and business performance improvement using the raw data collected from 33 selected manufacturing companies. In addition, it proposes modified MCPM model as an effective approach to improve business performance of manufacturing companies.

Design/methodology/approach:Research paper. Primary and secondary data were collected using questionnaire survey, interview and observation of records. The methodology is to evaluate business performances of sampled manufacturing companies and the extent of utilization of crucial non-financial (lagging) and non-financial (leading) performance measures. The positive correlation between financial business performance and practice of MCPM is clearly shown using Pearson’s correlation coefficient analysis.

Findings –This research paper indicates that companies which measure their performance using important financial and non-financial measures achieve better business performance. Even though certain companies are currently using non-financial measures, the researchers have learned that these financial measures were not integrated with each other, financial measures and strategic objectives.

Research limitations/implications: The limitation of this paper is that the number of surveyed companies is small to make generalization and they are found in a single country. Further researches which incorporate a large number of companies from various developing nations are suggested to minimize the limitation of this research.

Practical Implication: The paper shows that multi-dimensional performance measures with the inclusion of key leading indicator are essential to predict the future environment. But cost-accounting based financial measures are inadequate to do so. These are shown practically using Pearson’s correlation coefficient analysis.

Originality/value: The significance of multi-dimensional performance measures for business improvement in developing countries has been an issue among researchers. The originality of the paper is evident in the proposal of MCPM model, considering the problems being faced by some manufacturing firms leading to low performance.

Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.